Multiple Sensors for Monitoring Health and Wellness of an Animal

ABSTRACT

A system and method for monitoring the health of an animal using multiple sensors is described. The wearable device may include one or more sensors whose resultant signal levels may be analyzed in the wearable device or uploaded to a data management server for additional analysis.

FIELD OF THE DISCLOSURE

Aspects of the disclosure relate generally to animal safety, wellness,and health monitoring. More particularly, some aspects of the disclosurerelate to a viewing and system management system that monitors a pet'shealth and wellness.

BACKGROUND

Animals are far more stoic than humans and often do not complain ordemonstrate pain even while they are making adjustments to accommodatetheir distress. Through market research, pet owners have made it quiteclear that they do not need to be told that their pet is sick, butrather they need to know when their pet is getting sick and whatpreventative steps they should take in response. For example, if anowner knew her pet was getting sick, she could increase her level ofobservation (e.g., observe whether the animal is eating, drinking,and/or eliminating normally), increase or decrease certain activities(e.g., walks, etc.), and/or visit a veterinarian.

Similarly, veterinarians have very limited visibility into the health oftheir animal patients as most clinical encounters between a veterinarianand an animal patient are episodic in nature. As such, during normalcheckups veterinarians may not always perform or rely on certainreadings such as, e.g., blood pressure, respiration rate/variability, orcore temperature (sticking a thermometer in the animal's rectum) becausesuch readings may stress the animal further, may be difficult to perform(blood pressure), and/or are unreliable in a stressful clinical setting(animals may exhibit elevated readings in a veterinarian's office withother animals around—sometimes referred to as “white coat hypertension”or “white coat syndrome”).

Accordingly, some past solutions have attempted to remotely monitor ananimal in order to provide an animal owner with data relating to theanimal's health status while providing veterinarians further data toassist in diagnosing animal health conditions. However, each of thesepast solutions suffers drawbacks in that they do not provide acomprehensive view of the animal's health and do not provide an ownerand/or a veterinarian with adequate information to determine theanimal's health status.

Accordingly, there remains a need to provide a pet owner and/or aveterinarian with comprehensive information regarding a pet or otheranimal's current status such that the pet owner and/or veterinarian maybetter understand the wellness of a pet through non-invasive remotemonitoring in a stable home environment to pick up subtle vital signsindicators that could be precursors to developing health conditions.

SUMMARY

One or more aspects of the present disclosure relate to monitoring a petor other animal's health and wellness using two or more sensors in orderto provide a pet owner, veterinarian, or other party with content usefulin monitoring the pet's overall condition. Also, inferences based onanalyses of different signals from different sensors monitoring ananimal's vital signs, physiological signs, or environmental factors mayalso be provided. Some aspects of the disclosure provide a wearabledevice with embedded sensors whose operation may be governed by variousoperating modes and/or profiles in addition to the signals from othersensors.

The various aspects summarized previously may be embodied in variousforms. The following description shows by way of illustration of variouscombinations and configurations in which the aspects may be practiced.It is understood that the described aspects and/or embodiments aremerely examples, and that other aspects and/or embodiments may beutilized and structural and functional modifications may be made,without departing from the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present disclosure and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features.

FIG. 1 is a schematic diagram of a wearable device for a pet and itscomponents according to some aspects of the disclosure.

FIG. 2 is a functional block diagram illustrating the various types ofinformation received by the wearable device of FIG. 1.

FIG. 3 is a schematic diagram of a data management system and thevarious inputs thereto used in conjunction with the wearable device ofFIG. 1 according to some aspects of the disclosure.

FIG. 4 illustrates a collar incorporating the wearable device of FIG. 1.

FIG. 5 illustrates a cross-sectional view of an animal's neck wearingthe collar depicted in FIG. 4.

FIGS. 6A and 6B illustrate top and side views of an embodiment of thewearable device of FIG. 1.

FIG. 7 shows a harness incorporating the wearable device of FIG. 1.

FIG. 8 is a flowchart depicting basic sensor processing according tosome aspects of the disclosure.

FIG. 9 is a flowchart depicting processing of more than one sensoraccording to some aspects of the disclosure.

FIG. 10 is a flowchart depicting a sensor triggering other sensorsaccording to some aspects of the disclosure.

FIG. 11 is a flowchart depicting an illustrative example of how aninference may be formed using readings from different sensors accordingto some aspects of the disclosure.

FIG. 12 is a flowchart illustrating using readings from sensors from thewearable device and another sensor apart from the wearable deviceaccording to some aspects of the disclosure.

FIG. 13 shows a table with sensors and their related information inaccordance with one or more aspects of the disclosure.

FIG. 14 is a table with potential master/slave relationships of varioussensors identified in FIG. 13 in accordance with one or more embodimentsof the disclosure.

FIG. 15 shows an illustrative example of how the activation of thesensors of FIG. 13 may be modified in different operation modes inaccordance with one or more aspects of the disclosure.

FIGS. 16A-16G are illustrative examples of various sensors and how theirthreshold or thresholds, frequency of operation, and granularity may bemodified based on different profiles in accordance with one or moreaspects of the disclosure.

FIG. 17 shows an example of how various sensor profiles may be modifiedbased on breed information of the animal to which the monitoring devicesattached in accordance with one or more aspects of the disclosure.

FIG. 18 shows an embodiment with different operation modes of thewearable device in accordance with one or more aspects of thedisclosure.

FIGS. 19A-19B show the order in which operation modes take precedenceover profiles based on the embodiment of FIG. 18 in accordance with oneor more aspects of the disclosure.

FIG. 20 shows an alternative embodiment with different profilesincluding profiles replacing the operation modes of the embodiment ofFIG. 18 in accordance with one or more aspects of the disclosure.

FIGS. 21A-21B show the combination of different profiles of theembodiment of FIG. 20 with options of profile selection by one or moreswitches in accordance with one or more aspects of the disclosure.

FIG. 22 shows an illustrative example of how profiles may be selected inthe wearable device as well as in the DMS in accordance with one or moreaspects of the disclosure.

FIG. 23 shows an illustrative example of relevancy windows of readingsof on sensor in relation to other sensors in accordance with one or moreaspects of the disclosure.

FIG. 24 shows an example of different techniques for monitoring coretemperature including microwave radiometry and microwave thermometry inaccordance with one or more aspects of the disclosure.

FIG. 25 shows a display of various conditions of a monitored animal inaccordance with aspects of the disclosure.

FIG. 26 shows a specific display relating to one of the monitoredconditions of the animal of FIG. 25 in accordance with aspects of thedisclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in which thedisclosure may be practiced. It is to be understood that otherembodiments may be utilized and structural and functional modificationsmay be made without departing from the scope of the present disclosure.

General Overview

Aspects of the present disclosure are directed to a device worn by ananimal including one or more sensors for monitoring one or moreconditions of the animal and/or its environment. In some embodiments,the device may be a collar, harness, or other device placed on an animalby a human (e.g., a pet's owner). The wearable device may include aplurality of components including, e.g., one or more sensors and one ormore components used to transmit data as described herein. For example,in some embodiments, the wearable device may include a plurality ofcontact, semi-contact, and non-contact sensors for obtaining informationabout the animal, its location, and its environment.

Additional aspects of the present disclosure are directed to analysis ofthe different sensors. For the purpose of this application, at least twolocations at which the sensors are analyzed are described herein. First,the wearable device may analyze the sensor data. Second, a remote, datamanagement system (referred to herein as “DMS”) may process theinformation from the sensors. In addition, the DMS may process theinformation from the sensors in conjunction with additional informationfrom sources other than the wearable device including information fromancillary sensors proximate to the wearable device (includingstand-alone sensors and sensors attached to other devices, e.g., sensorsattached to or part of smartphones). Further, the DMS may receiveinformation from owners who have entered specific information based upontheir observations of the animal. In addition, the DMS may receiveinformation from third-parties including RSS feeds regarding ambientweather conditions local to the wearable device as well as data fromthird-party veterinarians or other service providers. It is appreciatedthat, in some implementations, the sensors may be analyzed only at onelocation or analyzed at three or more locations. The health-monitoringsystem may further use the owner observations of the animal collectedthrough, e.g., companion web/mobile based applications, telephone callcenter activity/teleprompts, and the like. The owner observations maycorroborate measured events (e.g., events measured by wearable device101 and/or one or more external sensors) to assist in lowering theongoing rate of false positives and false negatives. For example, insome embodiments, the health-monitoring system may include a mobileweight/size mobile device application which instructs the owner to wavea mobile camera integral to the mobile device across an animal with apre-identified marker in the field of view. Pre-processed data derivedfrom this action may then be uplifted to the DMS where conclusions canbe derived as to the animal's weight and size. Such data is thenappended to the animal's record. Other important owner recordedobservations may include observable items such as caloric intake, bloodin urine, black stools, smelly breath, excessive thirst, white skinpatches around the face, recording the disposition of the animal, andthe like. For instance, the caloric intake may be monitored by an ownerthrough an application running on a computer or smartphone in which theowner identifies what food and how much is being consumed over whatinterval.

Further, while described herein as being located remote from thewearable device, the DMS may be located on the owner's smartphone orlocated on the wearable device based on the respective processing powerof smartphone and wearable device. In these alternative embodiments, the“DMS” is identified by its ability to receive content from sources otherthan the sensors of the wearable device and process that additionallyreceived content for forwarding to the owner and/or veterinarian of thespecific animal. These alternative embodiments of the DMS are consideredwithin the scope of the “data management system” unless specificallyexcluded herein. For instance, if the wearable device is considered theDMS, the wearable device would receive data from its own sensors as wellas information from either sensors not located on the wearable deviceand/or additional content provided by the owner, veterinarian, or thirdparty.

Further, the veterinarian may provide information to the DMS 301including breed, age, weight, existing medical conditions, suspectedmedical conditions, appointment compliance and/or scheduling, currentand past medications, and the like.

For the purposes of this disclosure, some sensors are described as aspecific type of sensor in contrast to a more generic description ofother sensors. For instance, while the specification describes the useof a GPS unit providing location information, other location identifyingsystems are considered equally useable including GLONASS, Beidou,Galileo, and satellite-based navigation systems. Similarly, while thespecification describes the use of a GSM transceiver using GSMfrequencies, other cellular chipsets may be readily used in place of orin addition to the GSM transceiver. For example, other types oftransceivers may include UMTS, CDMA, AMPS, GPRS, CDMA (and itsvariants), DECT, iDEN, and other cellular technologies.

Also, for the purposes of this disclosure, various sensors andcombination of sensors are described as being co-located on the wearabledevice. However, in various situations, one or more sensors may never beused in a specific version of the wearable device. For instance,GPS-related sensors may not be useful for a version of the wearabledevice that is only to be used post-surgery in a recovery ward of ananimal hospital. Because precise location information is not needed whena veterinarian already knows the location of the animal (or even notuseable when in doors), a version of the wearable device with the GPSsensor disabled or not even included may be used. Similarly, othersensors may be disabled in (or never included in) this version of thewearable device where those sensors are not expected to be used. Forinstance, an RF signal sensor (one that determines if a beacon signalfrom a base station is above a predetermined threshold) may not beprovided in a version of the wearable device where that version of thewearable device is never expected to be used with a base stationemitting a beacon signal.

As used in this disclosure, the term “content” is intended to cover bothraw data and derived events. For instance, one example of the wearabledevice as described herein includes a profile/operation mode in whichraw data from various sensors are uploaded to a data management on acontinuous basis. Another example of the wearable device pre-processesinformation from various sensors and derives event information from thecombination of signals (or lack thereof) from two or more sensors. Thesederived events are referred to as “device-derived events” as theirderived in the wearable device. Similarly, the data management systemmay also derive events (referred to herein as “DMS-derived events”) fromcontent from the wearable device using only the raw data from thewearable device, the device-derived events, or a combination of both.Further, the DMS may further take into account content from ancillary orthird-party sensors to corroborate and/or further enhance theDMS-derived events. For instance, data from ancillary or third-partysensors may include audio files, image files, video files,radio-frequency identification (RFID) information, and other types ofinformation. To help correlate the data from ancillary or third-partysensors with data/device-derived events from the wearable device, thedata from the ancillary or third-party sensors may include timestamps.These timestamps permit the data management system to use the data fromthe ancillary or third-party sensors as if that data was part of thedata/device-derived events from the wearable device. Further, theinformation exchanged between the wearable device and the DMS and withthird-parties and (as well as with third-party devices) may be performedwith industry-standard security, authentication and encryptiontechniques.

The Wearable Device

FIG. 1 is an overview of wearable device 101 and its componentsaccording to some aspects of the disclosure. Wearable device 101 mayinclude several internal components, such as, e.g., ultra-widebandtransceiver (UWB) and other sensors described herein at least in FIGS.13-17. The sensors are represented in FIG. 1 as classifiable intovarious sensor types shown as Sensor Types A-F 110, 111, 112/113, 114,and 115. Although not shown separately in FIG. 1, the sensors arereferred to at times herein as N1 to Nm, with “m” being the total numberof sensors included in wearable device 101.

As shown in FIG. 1, wearable device 101 includes a processor 100 (ormultiple processors as known in the art) with firmware 102, an operatingsystem 103, and applications 104. The wearable device 101 may alsoinclude a storage 105 (e.g., a solid-state memory, Flash memory, harddisk drive, etc.). The wearable device may further include one or morean RF radio, a Wi-Fi radio, a Bluetooth radio, and/or a cellular radiotransceiver 107. The wearable device 101 may further include a localinput/output connection (e.g., USB, optical, inductive, Ethernet,Lightening, Fireire, status light or display etc.) 108, and a battery109. For purposes herein, local input/output connection 108 and theradio transceiver(s) 107 are generally considered “outputs” though whichinformation may be communicated to an owner or veterinarian directly(through sound emitter/status light/display 604 of FIG. 6), directly toa smartphone (via cellular, Bluetooth, or Wi-Fi or other communicationpathways) or though the DMS.

With respect to sensor types A-F, sensor type A 110 refers to the typesof sensors that have a sensor input 116 and no other internal components(e.g., simplistic photodiode). Sensor type B 111 refers to a sensor witha sensor input 117 and a processor 118 and storage 119 contained withinthe sensor type B. Here, sensor type B 111 may store data (at leasttemporarily) from sensor input 117 and process the data to provide amore meaningful result to processor 100. For instance, sensor B 111 maybe a UWB device for monitoring cardiac activity and the like based onmovement of a dielectric material (e.g., a heart muscle or othermuscle). Processor 118 may control the operation of the UWB andinterpret the results. In addition to monitoring cardiopulmonaryactivity, the UWB componentry may be used for core temperaturedeterminations and as a communication transceiver for communication witha network as known in the art for short distance, high bandwidthcommunications.

Further, as shown by dotted line 113, storage 119 may optionally beassociated with storage 105 to the point that processor 118 writesdirectly and/or reads directly from storage 105 (as being shared betweenprocessor 100 and processor 118). Raw data from sensor types C 112 andsensor types D 113 are processed by preprocessor 120 before the databeing sent to processor 100. Preprocessor 120 may be any type of knownprocessor that corrects/adjusts/enhances data. For instance,preprocessor 120 may be an analog to digital converter, an analog ordigital filter, a level correction circuit, and the like. Sensor type E114 includes any sensors not specifically identified above that provideresults from radar-based signaling (including RF signal strengthsensors, Wi-Fi IP address loggers, and the like). Finally, sensor type F115 includes battery sensors that provide data regarding the chargelevel and temperature of the battery 109.

Inputs to the Wearable Device

Processor 100 may be any known processor in the art that performs thegeneral functions of obtaining content from various sources inforwarding it through communication interfaces. The processor 100 mayalso perform specific functions as described herein. The communicationinterfaces may include one or more of microwave antennas, an RF antenna,and RFID antenna a cellular radio transceiver, and known hardwareinterfaces (for instance, USB). For example, processor 100 may directthe transmission on demand of data collected from one or more sensorsdue to an episodic event or may direct the transmission according to apredetermined schedule or when eventually connected to the DMS where thedata is collected in an off-line mode.

With respect to the off-line mode of operation, processor 100 receivesraw data from the various sensor types A-F 110-115. Next, depending onthe sensor and its current profile and/or operating mode, processor 100stores content relating to readings from the sensors. In a firstexample, processor 100 merely stores all raw data from the sensors. In asecond example, processor 100 only stores indications that a sensor hasprovided a reading outside of a normal range. The normal range may beset by the current profile and/or operating mode and may include one ormore thresholds for each sensor signal. For instance, an ambienttemperature sensor may have upper and lower thresholds of 28° C. and 15°C., respectively. If a reading from the ambient temperature sensorpasses one of these thresholds, that event is stored by processor 100and storage 105 identifying that the ambient temperature exceeded theidentified temperature range. In this example, either a binaryindication that the temperature range has been exceeded or the actualtemperature may be stored in storage 105. Further, to assist withsubsequent analyses by the wearable device 101 or analyses performed bythe DMS or third parties, processor 100 also timestamps the indicationthat the temperature has left the identified temperature range. In athird example, processor 100 may store in storage 105 both the raw datafrom the sensor leaving and identified range as well as the indicationthat the identified range has been exceeded. For instance, theindication may be one or more flags stored in storage 105 is associatedwith the sensor reading, the timestamp, and that the range has beenexceeded.

In a further example, processor 100 may operate in a low-power modewhen, for example, sensor F (the battery sensors 115) identify that thebattery is too hot and/or the battery is running low on available power.In this example, sensors that require significant power may be disabledor activated less frequently until the power level has been restored orbattery recharged.

Further, processor 100 may accept new software updates and change sensorthresholds, settings, etc., per instructions received from the datamanagement system DMS. The DMS is described below with reference to FIG.3. In addition, the owner may modify the thresholds to minimize when heis alerted to various sensor readings from the wearable device. This maybe permitted or restricted as minimizing some sensitivity may endangerthe animal when the owner should be alerted.

In some embodiments, wearable device 101 may be associated with a basestation (not shown). The base station may be capable of charging thebattery 115 of the wearable device 101. Further, the base station mayemit a steady beacon signal to wearable device 101 (but optional doesnot receive communications back from the wearable device 101). In someembodiments, the base station may be paired to a plurality of wearabledevices 101 (e.g., each worn by each one of a common pet owner'sanimals). In such embodiments, as known in the art with pairing ofwireless devices, each wearable device 101 may be paired to the basestation at the time of activation through a unique signal signature.Additionally, in some embodiments, each wearable device 101 may bepaired to multiple base stations. One of the benefits of using multiplebase stations is that, by comparing the relative strengths of signalsfrom the different they stations, the wearable device 101 may be able togenerally identify its location relative to the base stations (e.g., viatriangulation).

Optional Location Determination

In some embodiments, wearable device 101 may include a GPS receiver 106as one example of a sensor. The GPS receiver 106 may turn on once abeacon or other RF signal drops below a threshold level, in response toa sensed episodic event, on demand, or according to a predetermined timeschedule. Accordingly, the GPS receiver 106 may not be “always on” (andthus may not, e.g., consume power when GPS readings will not behelpful). By way of an example, if the signal strength of a beacon frombase station is high, then the wearable device 101 (and accordingly ananimal wearing wearable device 101) may be assumed to be located nearthe base station and thus the GPS coordinates of the animal may not bebeneficial to, e.g., the animal's owner. Accordingly, the GPS receiver106 may remain in an “off” state (e.g., powered down state) until, e.g.,processor 100 instructs GPS receiver 106 to turn “on” (e.g., when thesignal strength from the base station becomes weak or nonexistent).

The GPS receiver 106 may provide any useful information regarding thestatus of an animal wearing wearable device 101 including locationcoordinates of the animal, elevation of the animal, specific satelliteacquisition status, and the orientation of satellites. Some or all ofthis information may be used in sensor logic calculations and reduce GPSthrashing (continuous attempts to acquire signals and thereby drainingthe battery).

The processor 100 may use location information from the GPS receiver 106to identify a geo-zone (also refer to as a geo-fence) and determine whenthe wearable device 101 has left that identified area. For example, whenan animal wearing the wearable device 101 is playing off leash in apark, the animal's owner (using, e.g., a personal mobile device), theDMS, or other may prompt the GPS receiver 106 to create an instantgeo-zone around the location of the animal wearing wearable device 101.Accordingly, if the pet wanders too far (e.g., outside of thatgeo-zone), the owner (via, e.g., a signal sent from cellular radiotransceiver 107 to a personal mobile device), the DMS, or other may benotified that the pet has traveled outside of the geo-zone.

In embodiments where wearable device 101 is associated with a basestation, processor 100 may determine when, e.g., an RF beacon signal,Wi-Fi signal, Bluetooth signal, or other RF technology signal emittedfrom the base station drops below a threshold level and, in response,may obtain the location of the device from a GPS receiver 106 and recordand/or transmit the location of the wearable device 101 via a cellularradio transceiver 107, Wi-Fi, Bluetooth, or other technology to a petowner or veterinarian. Thus, according to one aspect of the disclosure,a location of an animal wearing the wearable device 101 may be easilydetermined when the animal strays too far from the stationary basestation. For non-cellular based radios, if the signal strength fallsbelow a certain threshold or is non-existent, processor 100 may changethe transmitting profile of the different modems to make them easier toeither locate or connect to various available networks or by a mobiledevice based application being used as directional finder.

In embodiments which include a base station, the health-monitoringsystem may further interpret readings coming from base station asdescribed herein. For example, signal strength of a beacon coming fromthe base station and received at wearable device 101 may be compared toa set of thresholds that have been set by the user or defaultsprovided/derived by the DMS during setup based on high, medium, and lowsettings. In some embodiments, during activation of the device and afterthe owner has set up the base station inside their premises, the usermay use a companion application (e.g., smartphone application) and walkaround her property holding the wearable device and geo-tag importantfeatures of her enclosure/yard/field, etc. At each location the GPScoordinates and beacon signal may be logged and uploaded to the DMS toassist in deriving the optimal safe proximity and geo-zones. The ownermay also acquire several other base stations that can be placed in otherlocations that the animal frequents (e.g., weekend properties, petsitter, etc.) or placed in several locations of a large and evenlyshaped property to create proximity zones of unique shapes.

Wireless Communications

The cellular radio transceiver 107 may be used as one means oftransmitting and receiving data at the wearable device 101. In someembodiments, the cellular radio transceiver 107 may provide presenceinformation on a cellular network and/or signal strength readings toassist in the wearable device's 101 logic calculations to preventthrashing (continuous attempts to acquire signals). Further, thecellular radio transceiver 107 may provide real-time clock adjustments,and may be used for cellular triangulation by the DMS when GPS signalsare not available or are at or below a usable threshold.

Inputs to the Wearable Device

FIG. 2 shows an illustrative example of various inputs usable by thewearable device 101. FIG. 2 shows RF signal 201, DMS inputs & triggers202, content from mobile companion apps/sensors 203, GPS-relatedinformation 204, device accessory content 205,Wi-Fi/Bluetooth/ANT-related information 206, cellular information 207,spectrum analyses 208, sound levels or actual recordings of sound 209,acceleration 210, core temperature 211, RFID (relating tointernal/external RFID-radios) 212, battery temperature/battery strength213, cardiopulmonary 214, ambient humidity 215, and ambient temperature216.

The RF signal 201 may receive signals including adjustable settings andoptions for, e.g., geo-tagging the boundaries of a pet owner's property,etc. as described above with respect to the beacon signal. In additionto or instead of RF antenna 107, wearable device 101 may include Wi-Fi,BLUETOOTH, and/or other RF technologies 206. TheWi-Fi/BLUETOOTH/ANT-related component 107 is intended to cover local,radio-based communication systems from body-worn to body-wide areanetworks.

Each may be used in conjunction with a GPS receiver 106 and/or cellularradio transceiver 107 or as a replacement to provide two-way datatransmission through paired access points as well as provide presence,proximity, and retrieve time of day information identifying the generallocation of the wearable device 101.

Wearable device 101 may further include an accelerometer providingacceleration signal 210. The accelerometer may be used to report levelsof specific activities of an animal. For example, readings from theaccelerometer may be interpreted as the animal being currently engagedin walking, running, sleeping, drinking, barking, scratching, shaking,etc. The accelerometer may be used to report the possibility of a highimpact event as well as corroborate and/or augment other sensorreadings. In some embodiments, the accelerometer may be used to controlother sensors (e.g., turn on, turn off, leave a breadcrumb, ignore areading). Further, the accelerometer may be used to determine which of aplurality of animals is wearing wearable device 101. For example, if apet owner uses a wearable device 101 interchangeably among more than onepet, a set of specific attributes pertaining to one of the animals maybe created and stored in storage 105 for each pet. Some of the storedattributes may be accelerometer data, such as a particular animal'sgait, and other attributes such as bark sound signatures. Storedattributes may be used to determine which pet is wearing wearable device101 by comparing currently sensed attributes to stored attributes.

Another sensor usable with the wearable device 101 is a light meter. Thelight meter provides the spectrum analyses 208 input of FIG. 2. In asimplistic example, the light meter may be tied solely to presence orabsence of a threshold of visible light. In a more sophisticatedexample, the light meter may be frequency-specific in its readings suchthat it can separately detect levels of infrared light, visible light,and ultraviolet light. Both of these examples of light meters of varyingsophistication are known in the art. In this environment, the processor100 uses signals from the light meter (or light meters) to determine ifthe wearable device 100 is located inside or outside. For instance,while a visible light level of a given intensity may indicate that thewearable device 100 is located under a bright light source (e.g., in asunny area), processor 100 may compare the current infrared and/orultraviolet light levels against the visible light levels. Accordingly,if the visible light level is high and the infrared and/or ultravioletlight levels are also high, then processor 100 determines that there isa likelihood that wearable device 101 is located outside in the sun.Alternatively, if the visible light level is high while the infraredand/or ultraviolet light levels are low, then processor 100 determinesthat there is a likelihood that wearable device 101 is located indoors(albeit in a sunny spot).

Further, the light meter may also be used to interpret light levels indetermining a current state of an animal to confirm or corroborate acurrent state of an animal. For example, in some embodiments extremelybright light incidences may be indicative of the animal wearing wearabledevice 101 being caught in a car's headlights, or being around gunfire,explosions, etc. as based on the sudden change in received light levels208. Identification of being caught in a car's headlights may be basedon a sudden spike in ambient light at night while the accelerometerindicates minimal movement before and after the spike in visible light.Further, a location determination (for instance, from a GPS receiver)may be used in place of or in addition to the accelerometer signal asaugmenting the determination of whether the animal has been illuminatedby oncoming headlights. Similar spikes in audio signals occurring withina short time of visible light spikes may be interpreted as being aroundthe gunfire, explosions, etc.

More advanced uses of spectrum analysis include the ability to detecttrace chemical signatures present in the animal's environment, emanatingfrom their skin/fur, orifices, and/or present in their breath. Forexample, readings could indicate dangerous environmental conditions(e.g., high readings of chlorine), skin related issues (e.g., yeast),and internal related conditions (e.g., ketones in the animal's breaththat may be exhibited before other symptoms are evident). Further, thespectrum analysis sensor may also be sniffing for chemical signatures.Combining the detection of sulfur with light and sound spikes helpscorroborate the determination that the animal has recently been locatednear gunshots or other explosions.

An ambient temperature sensor providing the ambient temperature 216 mayalso be provided as another example of a sensor. The ambient temperaturesensor may be used to determine a location of an animal wearing wearabledevice 101 (e.g., indoor versus outdoor). In some embodiments, theprocessor 100 tracks ambient temperature 216 over time and determines acurrent rate of change. If that current rate of change is greater than apredetermined rate as existing for a period of time, processor 100identifies the rate of change is a prediction that the animal wearingwearable device 101 will be overheating or freezing in the near future.Further, in some embodiments an ambient temperature sensor may be usedto corroborate or control other sensors.

The wearable device 101 may also include a humidity sensor providing theambient humidity input 215. In some embodiments, the humidity sensor maybe used to adjust sensed temperatures to wet bulb settings. These wetbulb settings may be important in calculating animal heat loss/gain andmay be used in roughly identifying a location of the animal (e.g.,inside or outside). Further, the excessive humidity or drynessidentified as signal 215 from the humidity sensor may be combined with atemperature reading to determine the heat index or wind chill.

Further, a microphone or peak noise detector sensor may provide soundinput 209. The microphone/peak noise sensor may be used to, e.g.,measure specific sound events (barking, etc.) and may be used tocorroborate other sensor readings. For example, in embodiments where alight meter indicates, e.g., an animal wearing wearable device 101 maybe caught in a vehicle's headlights; a microphone sensing a load noisemay be interpreted as, e.g., an impact event (getting hit by thevehicle). A specific method of determining an impact event is describedherein.

Another example of a sensor may be an internal battery strength and/orbattery temperature sensor 213 providing information regarding thestrength and/or temperature of the battery. The internal batterystrength and/or temperature sensor may be used to either modulatecertain other sensing activities and/or as an input source to othersensing activities. For example, in response to sensing the internalbattery is running low, GPS acquisition duty cycles and/or cellulartransmissions may be reduced to conserve power to extend the operationof the wearable device 101.

A core temperature sensor providing core temperature 211 may be providedas another example of sensor. The core temperature sensor may be used tonon-invasively measure the core temperature of an animal, and thusprovide data relating both to a real-time core temperature of an animaland an animal's change in core temperature over time.

The wearable device may also include one or more antennas as tied to oneor more of the internal radios/sensors. One of the internal componentsattached to the antennas is a UWB device. As known in the art, UWBdevice is used to monitor various conditions (e.g., used in fetalmonitoring, cardiopulmonary monitoring, and the like). Here, the UWBdevice may be used to monitor a variety of different conditions. Forexample, in some embodiments, the UWB device may be used to transmit andreceive UWB signals to non-invasively monitor operations of an animal'sheart. Signals from that monitoring operation are then processed byprocessor 100 to determine if an episodic event has occurred (e.g., anabnormally high heart rate), if a more complex event has occurred (e.g.,heat exhaustion after excessive running) and if the cardiopulmonarysystem of the animal is trending toward an undesirable condition (e.g.,an increasing average heart rate). Here, in addition to an average heartrate, a statistical deviation may also be provided. In this regard,statistical deviations may accompany other average rates as forwarded toveterinarians and possibly owners.

Specifically, the UWB device may be used to measure stroke volume and arelative change in blood pressure of an animal wearing wearable device101. For purposes herein, stroke volume readings from the UWB are usefulin addition to vital sign readings. In other embodiments, the UWB devicemay be used to determine if the wearable device is actually on theanimal. In some embodiments, a profile (e.g., stored characteristics) ofan animal may be available for more than one animal which wears thewearable device 101. In such embodiments, the UWB device may be used todetermine to which animal the wearable device 101 is currently attached.For example, readings at the UWB device may be compared to storedcardiopulmonary profiles to determine which of a plurality of animals iscurrently wearing the wearable device 101. Further, the UWB device maybe used to interpret changes in the neck tissue as indicative of ananimal eating, drinking, and/or vomiting. Further, the UWB device may beused to interpret signals in the abdomen area to investigate thepossibility of obstructions in the digestive track.

Any other desirable sensor may be provided as a component of wearabledevice 101 in order to measure one or more attribute of an animal and/orits environment. Those skilled in the art, given the benefit of thisdisclosure, will recognize numerous other sensors which may beincorporated into wearable device 101 without departing from the scopeof this disclosure. Further, the components and/or sensors containedwithin wearable device 101 may share some common circuitry such as powersupply, power conditioners, low pass filters, antennas, etc., as well asshare sensing data with each other to derive more meaning from combineddata sources.

According to some aspects of the disclosure, the wearable device 101(and associated base station(s), if any) and the DMS may form part of ahealth-monitoring system used to collect data about and/or monitorspecific health attributes of one or more animals. Further, in someembodiments, one of more of sensors may have the capability ofactivating, deactivating, controlling, rejecting, accepting, orthrottling another sensor's activities as described herein. In addition,the health-monitoring system may include both passive and active sensorsand multiple antennas that generate and receive a wide variety ofelectromechanical energy whereas the normal output of one or morecomponents may enhance the capability of another component in a derivedfashion.

The health-monitoring system according to some aspects of the disclosuremay further include external sensors (e.g., sensors external to thewearable device 101) which interact with or otherwise supplement thesensors of the wearable device 101. In some embodiments, these externalsensors may include detachable analog/digital items such as astethoscope, ultrasound sensor, infrared temperature sensor, pulseoximeter, blood pressure monitoring tool, glucose meter, blood analyzer,breath analyzer, urine analyzer, brain scanner (all which may includeadditional application software and/or be controlled by the devicesoftware), and filters/attachments to enhance/collaborate the existingset of sensors and readings. The individual operations of theseseparable sensors are known in the art. Here, wearable device 101provides a platform to which these additional sensors may be connectedand their data or analyzed content being stored in storage 105 forrelaying to an owner or DMS (or even third parties) as described herein.

In some embodiments, these external sensors may be integrally providedwith or associated with other well-known devices. For example, thehealth-monitoring system may collect data from a camera (with or withoutlens/filter attachments), microphone, speaker, GPS, and other items thatmay be plugged into or utilized by the wearable device 101 and/or thehealth-monitoring system. In some embodiments, these sensors may be partof a personal mobile device (e.g., a smartphone or the like). Each ofthese external sensors and/or mobile browser applications/installedapplications may act independently, in conjunction with the wearabledevice 101, may be triggered by the wearable device 101, or may betriggered by the DMS on a demand, episodic, or a scheduled basis toprovide additional and/or collaborative sensing information that willprovide important episodic, derived, or trending information to supportthe animals safety, wellbeing and health. In addition, all of the abovedescribed activities may be triggered by a mobile device and a companionapplications and attachments/accessories to provide time stampedcorrelation of sensor data as described herein.

Further examples of external sensors used in conjunction with thedescribed health-monitoring system may include RFID proximity sensorsthat communicate with RFID proximity tags and provide RFID content 212.For example, RFID proximity tags may be placed at an animal's bed, atits food bowl, at its water bowl, outside a door frame, outside a gatepost, near garbage cans, etc. Thus, when an animal wearing a wearabledevice 101 is near any of the above items, the wearable device(receiving a signal via the RFID sensor) may interpret that the animalis sleeping, eating, drinking, outside, out of the yard, getting intogarbage, etc.

The health-monitoring system may further use owner observations of ananimal collected through, e.g., companion web/mobile based applications,telephone call center activity/teleprompts, and the like. The ownerobservations may corroborate measured events (e.g., events measured bywearable device 101 and/or one or more external sensors) to assist inlowering the ongoing rate of false positives and false negatives. Forexample, in some embodiments, the health-monitoring system may include amobile weight/size mobile device application which instructs the ownerto wave a mobile camera integral to the mobile device across an animalwith a pre-identified marker in the field of view. Pre-processed dataderived from this action may then be uploaded to the DMS whereconclusions can be derived as to the animal's weight and size. Such datais then appended to the animal's record. Other important owner recordedobservations may include observable items such as caloric intake, bloodin urine, black stools, smelly breath, excessive thirst, white skinpatches around the face, recording the disposition of the animal, andthe like. For instance, the caloric intake may be monitored by an ownerthrough an application running on a computer or smartphone in which theowner identifies what food and how much is being consumed over whatinterval.

Further, the health-monitoring system may include sensors placedinternally within an animal (for instance, invasive but unobtrusivesensors). For example, microchips or the like embedded within an animalmay provide data relating to, e.g., blood oximetry, glucose monitoring,ECG, EEG, etc.

Data Management System

FIG. 3 shows an example of a data management system 301 receiving inputsfrom a variety of sources. Those inputs may be specific to an individualanimal or generally relate to related animals (related by one or morecharacteristics including breed, age, health condition, and the like).FIG. 3 shows data management system 301 receiving RSS feeds 302,Internet search content 303, social form content 304, content from chatswith veterinarians, symptom lookups and the like 305, cellularnetwork-related information 306, Wi-Fi/Bluetooth/ANT-related information307, wearable device 101-based sensors and accessories 308, third-partyelectronic services 309, veterinarian observations 310, content fromcompanion mobile apps/sensors 311, owner observations 312, andthird-party home tele-health sensors 313.

DMS 301 is a data receiving and processing system that receives dataand/or wearable device-derived events from the wearable device 101 andanalyzes that content directly, or in conjunction with older data orpast analyses of older data from the wearable device, or in conjunctionwith data from other sources, or any combination thereof. The DMS 301includes one or more processors, storage, operation software,input/output pathways, and the like as similar to that of the processor100 and storage 105 of wearable device 101 shown in FIG. 1. Further, theDMS may be a cloud-based computing platform in which communications viathe Internet are received in the DMS at a server or other hardwaredevice and processed in accordance with computer-executable instructionsand workflows. In this example, the DMS may have industry standardInternet connections, routers, servers, that connect DMS 301 to thevarious content sources 302-313. Alerts as sent to an owner compared toa veterinarian may be different. Further, even if the sensors areoperating as tied to a specific profile, the DMS may continue toseparate and forward alerts based on predefined settings at the DMS.

In some embodiments of the disclosure, the health-monitoring system mayfurther collect data using external rich site summary (RSS) feeds 302.For example, the system may receive data about the weather, environment,daily pet health tips, published research data, etc., via the RSS feed302. According to some aspects, this received data may be used tocorroborate, supplement, and enhance data collected from the wearabledevice 101, other external sources, and the like as discussed herein.

Some embodiments of the health-monitoring system may further receivedata from, e.g., non-invasive home telematics solutions 313. Forexample, the system may receive data from smart mats, smart motion/IFdetectors, and other devices prevalent in the marketplace. Pets andanimals inside a home may thus trigger these devices and thus recordsensor artifacts such as presence, weight, physiological signs, andvital signs. These recordings (which may normally be discarded by thehuman home monitoring systems) may provide valuable datacollection/corroboration points for the system, for example in the DMS(as described herein). Several techniques may be employed to upload thisdata to the DMS (e.g., companion mobile device application, user-enteredreadings, BLUETOOTH, Wi-Fi, other RF technologies).

When used as part of a health-monitoring system in FIG. 2 and asdescribed herein, the wearable device 101 may be the prime source ofsensor collected data (through, e.g., sensors and others describedabove). All sensors and their inputs may be available to beintelligently combined through data fusion to create meaningfulstandalone alerts and as an input into the DMS to develop and extracteven more meaning from the data.

In some embodiments, the health-monitoring system as described hereinmay include a DMS 301 remote to the wearable sensor 101 as schematicallydepicted in FIG. 3. In some embodiments, DMS 301 may receive informationfrom wearable device 101 and/or other sensors. Further, DMS 301 maytransmit information to, e.g., a pet owner (via, e.g., a computer,smartphone, tablet, land line, display of wearable device 101, statuslight/display/sound indicator 604 of FIGS. 6A and 6B, etc.) and/or aveterinarian (via, e.g., a web-based dashboard, facsimile, land line,mobile alerts, etc.). In some embodiments, DMS 301 may transmit dataaccording to predefined criteria. For example, according to someaspects, DMS 301 may transmit information periodically on a scheduledbasis. In other embodiments, DMS 301 may transmit information when thatinformation exceeds a threshold value. In still other embodiments, DMS301 may transmit data on-demand (e.g., requested by a pet owner,veterinarian, or the like).

In some embodiments, DMS 301 may be the data repository of all inputsregardless of the source to derive meaningful/actionable informationrelated to the animal's safety, wellness, and health for owners andveterinarians. In some situations, information specific to the animalwearing the device 101 (e.g., the third-party information service data309 and the third-party veterinary chat service data 311) may beforwarded from the DMS 301 to the third-party prior to receiving data(307, 311) from the third parties to assist with the third-parties'analysis. The DMS may analyze received data and determine the meaning ofthe data as DMS-derived events. Next, based on those events, the DMS mayobtain recommendations on file from a storage tied to those derivedevents, compile those recommendations, and provide the compiledrecommendations to the owner and/or veterinarian as actionableinformation. For instance, if the meaningful information is that theanimal has gained 5 lbs. in the past week and has exhibited a lower thannormal activity rate, the DMS 301 may look up recommendations on filefrom a storage tied to weight gain and the amount of weight gain and theidentified recommendation or recommendations. Next, the results arecompiled and forwarded to the owner/veterinarian as actionableinformation.

In general, the following lists typical inferences that may be reportedto owners: the animal is outside of designated safe zones; there is apotential situation where the animal may be overheating or freezing; theanimal may have been in an accident (high impact event of various levelsof severity); the animal's activity level has been decreasing even afterapplied filters for owner and pet lifestyle profiles; the animal islimping (based on a change in gait); the animal appears to be inpotentially dangerous environment based on extreme noise and lightindicators; the animal is very listless during sleep (as an indicationof pain, digestive issues, respiration issues, or past physiologicaltrauma); the animal's heart rate variability is abnormal; the animal'srespiration rate and quality is abnormal; the animal appears to be indistress/pain (yelps when there is large gross movement); and thewearable device is not on the animal that it was initially assigned toby means of examining its gate profile versus the one on file or othervital sign indicators that are part of their electronic profile.

Typical suggested actions may include to: increase the owner's personalobservations of the animal to confirm or dismiss specific developingitems of concern; increase/decrease thresholds for items in the animal'ssensor profile so they more closely align with the owner's and thespecific pet's daily life patterns, age, breed, size, and know medicalconditions; increase/decrease the animal's activity; monitor theanimal's diet (record caloric intake); remove the animal from apotential developing overheating/freezing situation; monitor the animalfor specific coughing sounds; refer the owner to specific relatedarticles/links/videos etc.; consult an optional online “ask-a-vet”services; and to see their veterinarian as soon as possible based on alife-threatening situation.

The following are illustrative examples of triggers that result inreporting issues to the owner: an episodic issue based on a sensor or agroup of sensors confirming an event comparing readings to presetthresholds; a time-based analysis (a.k.a a longitudinally-based) onanalysis at the device 101 level or the DMS 301 level based on trendingpositive or negative readings for a particular suspected condition; onthe demand of the owner or the veterinarian; periodically to provide asnapshot of the condition of the animal based on the owner orveterinarian's safety, wellness and health goals.

The veterinarian may receive a fewer number of inferences/suggestionsand more empirical data based on wellness issues and vital signs thatcould lead to serious health issues, the monitoring of specific knownhealth conditions, and the monitoring of the effectiveness of prescribedtherapies. The veterinarian may receive vital signs and otherphysiological information that suggests the animal is trendingpositively or negatively. Items that may act as triggers for theveterinarian to be sent information include an episodic vital sign(s)reading or physiological reading has passed its threshold or a derivedvital sign(s) or physiological sign or signs as trended over time havepassed thresholds set by the veterinarian.

Also, the veterinarian may be interested in the following currentpossible vital, environmental, or physiological signs: core temperature;ambient temperature & humidity; and core temperature. The veterinarianmay be interested in the following pulmonary information: detected lungmotion & measured respiratory rate and rhythm; measured respiration andexhalation times (ti/te); detected asymmetrical respiration(inflammation, obstructions, asphyxiation); measured chest compressionrate, depth, and chest recoil; and measured and ongoing monitoring ofchronic bronchitis. The veterinarian may be interested in the followingcardiac information: detected cardiac motion & measured cardiac rate andrhythm; measured changes in cardiac stroke volume and cardiac output; acomparison of blood pressure to a threshold; signs of developingcongestive heart failure; signs of bradycardia and tachycardia; signs ofhemo/pneumothorax. Further, the veterinarian may be interested in thefollowing other information: signs of a seizure; uterine contractionrate and intensity; identification of possible sleep problems such assleep apnea; signs of a foreign body in the animal; long-term sensordata; average and statistical deviation of cardiac activity, respirationactivity, and core temperature; activity level; estimated weight;estimated hydration levels; and average daytime/nighttime ambienttemperatures. The following are sample inferences that may be derived bythe DMS 301 and identified to the owner or veterinarian for diagnosis:heartworm; vomiting & diarrhea; obesity; infectious diseases; kennelcough & other developing respiratory conditions; lower urinary tractinfection; dental disease; skin allergies; damaged bones & soft tissue;cancer (for instance, by ketone level changes in the animal's breath);developing heart conditions; distress/pain; and cognitive dysfunction.The following are sample symptoms/inferences made from a combination ofsensor data and veterinarian-supplied data: impact of specificprescribed therapies; recovery status of an animal who has justundergone surgery; and trending of vital signs against a base linedetermined by the veterinarian.

In such capacities, the DMS 301 may be receiving raw data, pre-processeddata at the wearable device 101 level. For example, the accelerometer{x,y,z} g values may be averaged over a fixed window (for instance, aone second window), a deviation of magnitude computed, and a high,medium, or low activity designation may be assigned based on theactivity of the animal. Sound files from a separate device, RSS feeds,and other unlike data types need to be catalogued, time stamped, sortedand prepared for analysis. Because the DMS receives these divergenttypes of data, the DMS 301 may perform these correlations. For instance,the DMS 301 may receive high ambient temperature readings from thewearable device 101 and compare it against expected local temperatures(obtained by RSS feed 302 or Internet search 303) for the current orlast identified location of the wearable device 101. If the ambienttemperature is high (for instance, over 45° C.) while the predicted hightemperature for the location is only 20° C.), then the DMS 301 mayderive that the animal is locked inside a car with its windows shut.Based on this derived event, the DMS may attempt to alert the owner byvarious means including email, SMS or other text messaging systems,social messaging systems (like Twitter and Facebook, etc.) or by callingthe owner directly. It is appreciated that the frequency and thresholdsfor alerts may be fixed or may be configurable by the user.

DMS 301 may also include information about past events, current events,or predictions of possible future events. DMS 301 may also act as thecommunications hub between the wearable device 101 and third partyservices, the vet, and/or a pet owner through various communicationschannels and devices. For example, in some embodiments a pet owner mayuse her personal mobile device as an input device to record her ownobservations through free form text or drop down menus (effectivelybecoming one sensor of the sensory platform) and thus DMS 301 receivesthese inputs from the owner via the personal mobile device. Each dataelement stored in the DMS 301 may be meta-tagged so that each standsalone without having to go back to, e.g., an owner/pet profile. Suchmeta-tags may include a time stamp, geographical data, breed, age, etc.,that may facilitate large scale anonymous data analysis.

Neck Placement of Wearable Device 101

FIG. 4 illustrates a collar 402 including wearable device 101 accordingto one aspect of the disclosure. As depicted in FIG. 4, collar 402 mayinclude wearable device 101 such that the wearable device 101 ispositioned near the neck of animal 401. Accordingly, in such anembodiment, sensors receive data near the neck of animal 401 at sensinglocation 402. Further, wearable device 101 receives and transmits dataat transceiving location 404.

FIG. 5 illustrates a cross-sectional view of animal's neck wearingcollar 402 including wearable device 101. As depicted, collar 402 mayinclude a clasp 505 that, when clasped, positions wearable device 101adjacent to fur 501 on the lower side of animal's neck. FIG. 5 depictsapproximate locations of the structures within the animal's neck.Specifically, FIG. 5 shows carotid arteries 503, jugular veins 504,esophagus 509, trachea 511, and spinal column 510 in relation to thewearable device 101. In such a configuration, antennas of thecardiopulmonary (e.g., UWB device) and other inward-looking components(e.g., ECG and ultrasound probes) contained in wearable device 101 areplaced on the inside of collar 402 while processor 100, other sensors,and other components (e.g., RF antennas 107, RFID antennas 111, etc.)are located on the other side of collar 402 (for instance, at location507). Further, the outward looking antennas may be located at any oflocations A-I to help minimize interference with the inward-lookingantennas. Alternatively, sensors located at locations A-I may haveimproved readings by separating them from interference with contact withthe animal. For instance, if the ambient temperature sensor was placedat location A, there is a potential for errant readings when the animalis laying on its chest and wearable device 101 is resting on theanimal's paw. Locating the ambient temperature sensor at an alternativelocation, for instance, D-I, may improve the reading from the sensor asit would be spaced from the animal's paw when the animal is laying inthis position. Further, in an alternative example, various sensors maybe replicated around the collar 402 and their readings averaged or thehighest and lowest readings dropped to reduce the influence of aberrantreadings.

As shown in FIG. 5, wearable device 101 is able to receive and transmitinformation on the outside of collar 402, while keeping inward-lookingantennas near animal's skin on the inside of collar 402 such thataccurate readings from, e.g., the animal's carotid arteries 503 and/oresophagus 509 may be obtained. Alternatively, readings may be obtainedfrom jugular veins 504 instead of or in conjunction with carotidarteries 503. Other tissue movement may also be of interest includingmuscle movement surrounding the trachea (as the trachea's cartilage maynot be reflective of some dielectric signals and not detectabledirectly).

The configuration of wearable device 101 according to some embodimentsof this disclosure may be more readily understood with reference toFIGS. 6A and 6B. FIG. 6A illustrates a top view and FIG. 6B illustratesa side view of an embodiment of wearable device 101. In the embodimentof FIGS. 6A and 6B, wearable device 101 may include two portions: aninside portion 601 and an outside portion 603. Inside portion 601 mayinclude the inward-looking antennas such as the UWB antennas, microwaveantennas, or ultrasound antennas. For instance, the antennas may belocated at locations 605 and 606. Outside portion 603 may include othercomponents such as processor 100 and the other components of FIG. 1including outward-looking antennas. In one example, the inward-lookingantennas of portion 601 may be shielded from the outward-lookingantennas of portion 603 by a metal or metallized layer or other knownantenna isolation material to minimize interference between thedifferent sets of antennas. Further, status information including on/offstatus may be provided to the owner via status light 604. Status light604 may be a simple LED or may include a display screen and touchinterface configured to display content to an owner as opposed to (or inaddition to) sending the information to the DMS to then be forwarded tothe owner's smartphone. In addition, 604 may be a sound generator thatresponds to setting changes.

When wearable device 101 is placed on an animal, such as shown in FIG.5, the inward-looking antennas will be located near the animal 401(e.g., inside of collar 402) and thus provide accurate sensing, whileother components, including some components used to transmit and receivedata, may be placed away from animal 401 (e.g., outside of collar 402)such that transceiving capabilities of the outward-looking antennas arenot degraded by the operation of the other antennas.

Further, metal or metallized probes 610 and 611 may be used to establishprobe-to-skin contact for sensors that may be improved with direct skincontact. These types of sensors may include skin temperature sensors,heart rate sensors, and ECG sensors. With respect to temperaturesensors, these probes may be attached to one or more heat-sensingcomponents (or may include those heat-sensing components. The heatsensing components may include thermistors, thermocouples, and the likeand combinations thereof.

Chest Placement of Wearable Device 101

In other embodiments, wearable device 101 may not be worn around a neckof an animal 401, but rather may be worn at any suitable location forreceiving information by the sensors. For example, and as illustrated inFIG. 7, wearable device may be provided as part of a harness 701 wornaround animal chest. In such an embodiment, sensing location 703 andtransceiving location 704 will be near animal's chest rather than nearanimal's neck (as depicted in FIG. 4). Regardless of the particularlocation of wearable device 101 (at the neck location or chest location,batteries 115 and other detachable components may be removable andreplaceable by a pet owner 705.

Operation of Sensors

FIGS. 8-12 and 22 relate to flowcharts showing processing of thewearable device 101 and/or DMS 301. These flowcharts are used to explainvarious aspects of analyzing signals from one or more sensors. It isappreciated that other types of analyses based on the sensor informationare possible in place of threshold comparison. Other known techniquesinclude Bayesian inference analysis, neural networks, regressionanalysis, and the like and their use to analyze the signal inputs areencompassed within the scope of this disclosure.

Turning now to FIG. 8, a flowchart representing basic sensor processing(e.g., processing of one or more internal sensors, external sensors,internal sensors, and/or other sensors) is depicted. A sensor processedas shown in FIG. 8 may be one that is either on all of the time,interrupt driven, or triggered on demand. At step 801, sensor data isreceived from sensor n. Again, this sensor data may be continuouslyreceived (e.g., always on), may be triggered by another sensor's reading(e.g., interrupt driven), or may be received in response to a pet owner,veterinarian, or the like requesting sensor data (e.g., on demand). Atstep 803, the received sensor data is compared to a threshold value. Atstep 803, the relationship of the compared data to the threshold valuemay be such that nothing of interest is happening. In such a situation,the data may be ignored as indicated by step 809, and the method willreturn step 801 to receive additional data. However, if the compareddata exceeds the threshold, this occurrence is written to storage instep 805. Optionally or in addition to step 805, an alert may beprovided to a pet owner or sent to the DMS as shown in step 807. Thealert may be local (e.g., an audible alarm on the wearable device 101)and/or may be remote (e.g., on a pet owner's personal mobile device,within a veterinary dashboard, etc.). In a further modification, thefact that the signal from sensor n did not exceed the threshold may alsobe stored as shown in broken lines from the NO output of determinationstep 803 to the ignore step 809 as a positive indication that thereading was within the threshold. Further, the series of store ratingsprovide a breadcrumb data set of incremental changes that may be usableby the DMS.

FIG. 9 depicts an embodiment where readings from multiple sensors {n1,n2, and n3} may be used to determine a status of an animal. Again, eachof the sensors in the diagram may be constantly on, interrupt drive, ortriggered on demand. At steps 901, 903, and 905, data is collected fromeach sensor n1 through n3. As discussed, the sensors may be located inwearable device 101 and/or external devices (e.g., smartphone, RSS feed,etc.). Any one of sensors n1, n2, and n3 may individually trigger analert condition in step 906, and written to storage in step 907 and(optionally) the alert provided to the owner or DMS in step 909.Otherwise, the determination is ignored in step 908. Similar to theprocess of FIG. 8, data may be breadcrumbed despite the sensor readingsnot exceeding a threshold as shown in the broken lines from step 906 tostep 907 and then back to step 908.

Alternatively, step 906 may require a consensus of all three readings aweighted basis is needed to either confirm an alert condition or ignorethe sensed the data. For example, at step 907, in response to one ormore of sensors n1, n2, and/or n3 triggering an alert condition at steps901, 903, and/or 905, respectively, a combination of the sensed datafrom each sensor is compared to one or more thresholds to determine if,e.g., an alert condition is present. Further, at step 907 the sensedreadings may be compared to past readings that are either stored locally(e.g., within wearable device 101) or stored, e.g., in the DMS 301.Thus, using the sensed data from multiple sensors (in the depictedembodiment, n1 through n3), inferences regarding animal and pet safety,wellness, and health may be formed at step 907 based on analysis of thesensor's readings and/or, e.g., breadcrumbs (time-stamped recordings).If the combination of the sensor data triggers an alert (e.g., if thecombination of data confirms an alert condition), the alert may bereturned at step 909 (to, e.g., a pet owner and/or veterinarian, etc.).However, if the combination of sensor data does not trigger an alertafter being compared to one or more thresholds, the data is ignored atstep 908 and the method returns to steps 901/903/905 to receive furtherdata. In any event (e.g., alert or ignore) the readings and results maybe written to local storage at step 907 for subsequent upload to the DMS301.

The analysis of the sensor data at step 803 or the multiple sensor dataat step 907 may be performed in any suitable location within the system.In some embodiments the analysis may be performed in the wearable device101. In such embodiments, wearable device 101 may perform episodic dataanalysis (e.g., independent intelligent decisions) as well aslongitudinal data analysis. For the latter, the wearable device maymonitor a number of recorded breadcrumbs of various events over time.For example, the wearable device 101 may monitor the animal'stemperature over time in order to monitor the animal's condition incompliance with FAA regulations on pets stored in cargo holds. In otherembodiments, the wearable device 101 may monitor the animal's barkingover time to ensure the animal 401 is complying with local by-laws or tointerpret continued barking as a potential stress indicator.

In other embodiments, the analysis of the sensor data may be performedin DMS 301. Again, DMS 301 may perform both episodic data analysis aswell as longitudinal data analysis. For the latter, DMS 301 may look atindividual events, combined events, and derived events (e.g., calorieintake versus activity levels). By looking at such events in the DMS301, patterns of animal's 301 health and wellness may be determined. Forexample, the DMS 301 may determine patterns of improvement (or lackthereof) of an animal following a drug or therapy treatment of animal401 after it has left the veterinarian. Further, the wearable device 101data may be combined with sensors from other sources (e.g., RSS feeds302, owner observations 312, etc.) in performing the analysis. Forexample, an RSS feed 302 including the number of degree days may becompared to a number of high temperature alerts at a wearable device 101to determine if, e.g., animal 401 is overheated or if, rather, it isjust an abnormally warm month. As another example, owner's observations312 (e.g., observations of staggering after exertion, unusual fatigue,abnormal coughing, pale gums, etc.) may lead the DMS 301 to modify theprofile or operation mode of the wearable device to employ profiles withfiner granularity and sensing more often and with more sensitivethresholds for cardiopulmonary algorithms at the wearable device 101level.

As presented in FIGS. 8 and 9, an analysis of an animal's health andwellness may be performed by analyzing data from an individual sensor(e.g., FIG. 8) or from the combination of two or more sensors reading atthe same time (e.g., FIG. 9). In other embodiments, analysis of ananimal's health and wellness may be performed by one or more sensorstriggering one or more additional sensors in order to corroborate thedata of the first sensor. This may be more readily understood withreference to FIG. 10. As shown in FIG. 10, data is received from onesensor (in the depicted embodiment, n1) at step 1001. This data iscompared to one or more thresholds at step 1003 as described withrespect to FIGS. 8 and 9. If the sensor reading does not exceed athreshold (e.g., is not interesting) then the data is ignored at step1007 and the method returns to step 1001 to obtain additional data.Alternatively, the data may always be stored/written locally at step1005 for later upload to DMS 301.

If the data from sensor n1 obtained at step 1001 does exceed one or morethresholds at step 1003, then signals from additional sensors may bechecked to confirm or corroborate the received data from step 1001. Thatis, in some embodiments, one or more sensors (in the depictedembodiment, n1) may act as a “master” sensor after it has sensed athreshold level, and then subsequently control additional “slave”sensors. Here, steps 1001-1009 are related to the operation of themaster sensor n1, collectively identified by the dashed box 1000M.Similarly, steps 1010-1014 are related to the operation of the slavesensors n2 and n3, collectively identified by the dashed box 1000S. Inthe depicted embodiment, once data collected at step 1001 exceeds athreshold at step 1005, additional slave sensors are triggered tocollect data at step 1010 (n2) and step 1011 (n3) or their previouslycollected data checked. At step 1012, analysis of the received data(e.g., data received at steps 1001, 1010, and/or 1011) may be performed,and an inference may be made regarding animal's health and wellness.Further, the data received from each sensor (n1, n2, and n3) mayoptionally be weighted or otherwise adjusted to determine an inferenceregarding an animal's health and/or wellness as described herein. If, atstep 1012, the combined data does not exceed a threshold level (e.g.,the further data collected at steps 1010 and/or 1011 does not confirmand/or rather negates an inference made at step 1003), then the data maybe ignored at step 1007 and the method thus returns to step 1001 tocollect new data and thus continually monitor animal 401. However, ifthe data collected at steps 1010 and/or 1011 confirms or supplements theinference made from the data collected at step 1001, then thisdetermination is recorded in step 1013 by writing this determinationinto storage 105. Further, an alert may be returned to the animal'sowner and/or a veterinarian at step 1014. Again, regardless of theinference made (e.g., ignore versus alert) the data may bewritten/stored locally at step 1013 for future upload to the DMS 301.

The methods described in FIGS. 8-10 (e.g., inferences made from a singlesensor or a combination of sensors) may be used arrive at specificinferences of an animal's health or wellness. For example, the analysisof one or more sensors Nm may allow episodic and/or longitudinalinferences to be made regarding animal's health and wellness. As anexample episodic inference that may be made using one or more sensors,in one embodiment a GPS geo-zone alert may be confirmed or canceledusing, e.g., GPS sensor (as one example of the sensor provided onwearable device 101). Specifically, a geo-zone alert may be prone tofalse positives due to, e.g., temporary loss of communication with oneor more satellites (which may thus be interpreted as movement of animal401). However, in some embodiments, a GPS geo-zone alert may be comparedwith an accelerometer reading to corroborate/confirm the alert.Specifically, if the animal 401 is not moving (as determined from datareceived from the accelerometer) the geo-zone alert may be canceled.

Similarly, in some embodiments signal strength of, e.g., an RF signalmay be compared to GPS position of animal 401 to confirm, e.g., a breachof a geo-zone. Specifically, a reading from the GPS may be indicativethat the animal 401 has moved outside a geo-zone. However, if signalstrength of an RF signal from a base station (received at RF antenna) isstill rather strong, the GPS readings may be interpreted as a falsepositive (e.g., the result of losing communication with one or moresatellites) and thus the alert may be canceled.

As another example episodic inference that may be made using one or moresensors, a reading of high acceleration (from, e.g., an accelerometer)may trigger additional sensors and/or otherwise be compared with datafrom additional sensors to determine if animal 401 was involved in animpact event (e.g., being hit by a vehicle). For example, a reading ofhigh acceleration from the accelerometer may be supplemented with areading from, e.g., a light meter and or a microphone on wearable device101 (as two examples of internal sensors). If, in addition to the highacceleration reading, the wearable device received a high lightincidence reading (e.g., headlights) and/or a high noise reading (e.g.,impact) then an alert of a possible impact event may be returned.

As another example episodic inference that may be made using one or moresensors, a breach of a perimeter fence (as determined by RF antenna 107,Wi-Fi, BLUETOOTH, or other RF technology) may be compared to readingsfrom an ambient light, sound, temperature, and/or humidity sensor onwearable device 101 (as examples of internal sensors) to determine ifanimal 401 has in fact, e.g., left a house. If the sensed humidity,temperature, light, etc., is indicative of the animal 401 being outside,then the perimeter fence alert may be returned. However, if each readingis indicative of the animal 401 being inside, the breach of perimeterfence alert may be interpreted as a false positive and thus canceled.

As another example episodic inference that may be made using one or moresensors, data from, e.g., a microphone (as one example of a sensor) maybe compared with reading from an accelerometer (as another example of asensor) to determine if animal 401 has been, e.g., barking longer than athreshold period of time. For example, a reading from a microphone maybe indicative of animal 401 barking, or may be due to some other event(e.g., thunder). However, data received from the accelerometer mayconfirm/negate that the animal has been barking according to whether ornot a signature head movement or vibration of a barking event was sensedor not.

Further, sensed data from an inward looking antenna (e.g., a UWBantenna) may be compared with a microphone to form many inferencesrelated to respiration quality and the like. For example, UWB antennamay be used to form an inference of animal's respiration quality bymonitoring movement of muscles in the neck area (e.g., the musclessurrounding the animal's trachea 511). Further, the sensed UWB data maybe corroborated with a microphone located on wearable device 101 and/oran external microphone (e.g., a microphone located on an owner'spersonal mobile device such as a smartphone, etc.) to make an inferenceregarding whether the animal 401 has kennel cough, bronchitis, etc.

As another example episodic inference that may be made using one or moresensors, noninvasive cardio output may be determined by measuring bothheart rate (beats per minute), quality (fluctuations over the minute),and stroke volume to provide cardiac output using UWB technology oneither an episodic or trending basis. Other derived conclusions fromthese measurements may also include a change in blood pressure over timeand whether the animal is losing blood volume due external or internalbleeding. These sensors may be placed on the animal's chest near thesternum, at the front of the neck near the wind pipe and carotidarteries, or on other parts of the animal to pick up specific signals ofinterest.

As another example episodic inference that may be made using one or moresensors, noninvasive core temperature may be measured and/or derivedfrom several internal and ambient thermistors. Further, microwaveradiometry/thermometry (using a microwave antenna) along with othertechniques may be used to determine fluctuations in core temperaturewhich may be indications of hypothermia, hyperthermia, bacterial orviral infections, inflammation, on set of disease, immune-mediated orneoplastic diseases, extreme exercise, or ovulation.

As another example of an episodic inference that may be made using oneor more sensors, noninvasive measurement of blockages in the digestivetrack can be accomplished by moving the wearable device 101 to the areaof concern to allow readings and an upload of data from this activityusing the UWB technology.

As another example episodic inference that may be made using one or moresensors, noninvasive measurement of the animal's drinking and eatinghabits may be measured independently or corroborated with other sensorsusing UWB technology by examining signals from the neck area includingthe esophagus and surrounding tissues.

In some embodiments, a base line measurement of animal 401 may bedetermined and then compared to subsequent data collection to determine,e.g., one or more of the inferences discussed herein. In someembodiments, data received from two or more sensors may be used todetermine, e.g., that it is an appropriate time to collect this baselinedata. For example, in some embodiments, a clock or other component(e.g., light meter, etc.) may be accessed to determine, e.g., that it isnight time. Further, data from the accelerometer may be referenced toconfirm that, e.g., animal 401 is sleeping (as indicated by no or littleacceleration). In such embodiments, a baseline measurement of one ormore vital signs and/or physiological signs may be taken in response tothe one or more sensors indicating that animal 401 is sleeping.

The above methods of determining episodic inferences from one or moresensors may be more readily understood with reference to a specificexample. In one embodiment, wearable device 101 may include anaccelerometer, a microphone (as examples of internal sensors) and/orcardiopulmonary sensors (e.g., UWB device). In such an embodiment, theaccelerometer may measure a high acceleration event, and the wearabledevice 101/DMS 301 may interpret the acceleration as indicative of apossible impact event (e.g., the animal 401 was hit by a vehicle). Thewearable device 101/DMS 301 may then corroborate or confirm thisinterpretation by referencing other sensors, e.g., microphone. Forexample, if the microphone sensed a loud noise at the moment of the highacceleration, the inference of an impact event may be confirmed. Thismay then trigger other sensors, such as cardiopulmonary sensors (e.g.,UWB device). For example, the cardiopulmonary sensors may check animal401 for anomalies, which may include, e.g., checking animal 401 for lossof blood volume (indicative of, e.g., internal or external bleeding).

The example of an episodic inference of an impact event made by thewearable device 101 and/or DMS 301 is illustrated in FIG. 11. FIG. 11illustrates how readings of one or more sensors may be interpreted asindicating that an event has occurred. As it shown in FIG. 11, signalsfrom five sensors are used with the sensors identified as Na, Nb, Nc,Nd, and Ne, respectively. The readings from sensors Na 1101, Nb 1102,and Nc 1103 are weighted independently by weighting factors W_(Na) 1104,W_(Nb) 1105, and W_(Nc) 1106, respectively. Next, in step 1107, it isdetermined if the weighted combination of the readings of these threesensors is above a threshold a1. If no, then the system ignores thesensor readings in step 1108 and returns to monitoring the animal. Ifyes, then this determination is stored in step 1109 and the alertprovided as alert level 1 in step 1110.

FIG. 11 also includes the ability for determination of a second alertlevel (alert level 2). For instance, the system knows after step 1107that alert level 1 has been reached. The system may additionally checkin step 1111 the weighted combination or perform an additional weightingand compare the weighted combination against a second alert levelthreshold, here, the a2 threshold. If yes from step 1111, that is secondalert level a2 is stored in step 1112 and alert level 2 is identified tothe owner/DMS in step 1113.

If no from step 1111 as having not found a second alert level based onthe initial weighted sensor readings from sensors Na, Nb, and Nc, theremay be additional sensor inputs that allow a determination that thesecond alert level has been reached. For instance, sensor readings fromsensors Nd 1114 and Ne 1115 may be obtained. For the sensor reading fromsensor Nd, the system determines in step 1115 if the sensor reading isbelow a low threshold for sensor Nd. If yes, then this determination isstored in step 1112 and the alert level 2 is provided in step 1113. Ifno from step 1115, the system determines in step 1116 if the sensorreading is above a high threshold for sensor Nd. If yes, then thisdetermination is stored in step 1112 and the alert level 2 is providedin step 1113. If no from step 1116, then the system continues to providethe alert level 1 in step 1110.

A similar determination may be made for reading from sensor Ne. For thesensor reading from sensor Ne, the system determines in step 1118 if thesensor reading is below a low threshold for sensor Ne. If yes, then thisdetermination is stored in step 1112 and the alert level 2 is providedin step 1113. If no from step 1118, the system determines in step 1119if the sensor reading is above a high threshold for sensor Ne. If yes,then this determination is stored in step 1112 and the alert level 2 isprovided in step 1113. If no from step 1119, then the system continuesto provide the alert level 1 in step 1110.

Finally, one of the original sensor levels may be reviewed to determineif it is outside of a profile for that sensor. For instance, in step1120, the sensor readings of sensor Nc are compared against a profilefor that sensor. If the readings are outside of that profile, then thisdetermination is stored in step 1112 and the alert level 2 is providedin step 1113. If no from step 1120, then the system continues to providethe alert level 1 in step 1110.

The following explains how FIG. 11 may be applied to specific sensorreadings to determine if an event has occurred. The following exampleexplains how a determination is made that a high impact event hasoccurred. Here, sensors Na, Nb, Nc, Nd, and Ne are represented by alight meter sensor n1, a microphone/peak sound sensor n2, anaccelerometer n3, a GPS receiver n4, and a cardiopulmonary sensor n5,respectively.

At step 1103, accelerometer (n3) senses a high acceleration event (e.g.,10+ G's) potentially indicative of a high-impact event. In thisembodiment, the accelerometer (n3) acts as a “master” sensor such thatwhen it has sensed this episodic condition at step 1103 (e.g., highaccelerations possibly indicative of an impact event), it may controlthe sensing and/or data reporting of other sensors toconfirm/corroborate the event. Specifically, processor 101 may use thehigh signal on accelerometer n3 to look back for recent readings fromlight meter n1 and microphone n2. Those recent readings may have beenstored in storage 105 or in storage 119, depending on the sensor. Theeffect is that accelerometer sensor n3 is, for this instance, a mastersensor and the light meter n1 and microphone n2 are the slave sensors.

The previous readings from the slave sensors are reviewed to look forepisodic threshold events to create a more accurate picture as to whathas transpired over the previous time interval and possibly confirm apossible high impact event from accelerometer n3. Thus, at step 1105processor 100 retrieves stored data from the microphone/peak soundsensor (n2) for a time period immediately preceding and overlapping withthe high acceleration reading, and at step 1107 processor 100 retrievesstored data from the light meter n1 for a time period immediatelypreceding and overlapping with the high acceleration reading.

At steps 1104-1106, the data received from each sensor may be weightedand combined into a single result to determine in step 1107 if theconstructed profile meets a high degree of probability that an event ofinterest (e.g., impact) has occurred. For example, if the light meter(n1) sensed a high incidence of light (potentially indicative ofheadlights), and/or if the microphone/peak sound sensor (n2) sensed aloud noise (potentially indicative of a being impacted by a vehicle),then the method may determine at step 1107 that an impact has in factoccurred. If the other readings do not confirm the possible impactevent, then the data may be ignored at step 1108. Regardless, the datareceived may be written and/or stored locally at step 1109 forsubsequent upload to the DMS 301.

If the combined and corroborated data meets certain conditions (e.g.,each is indicative of an impact event) in step 1107, the master sensor(in the depicted embodiment, accelerometer n3) may trigger and/or changestates other sensors (including itself) in order to, e.g., takeindividual spot readings, schedule-based readings, or change eachsensor's sensing configurations. If the readings are inconclusive, thesensors are instructed to continue reading.

For example, in the depicted embodiment, at step 1109, the accelerometer(n3) changes (as being controlled by processor 100) from being in aninterrupt mode (e.g., looking for episodic events) to a real-timemonitoring of motion activities. This real-time monitoring may becompared to a profile to determine if the animal's gait has changeddramatically as determined in step 1120. At step 1117, the GPS sensor(n4) is instructed (i.e., controlled by processor 100) to determinelocation, speed, and/or direction of the animal 401. If the animal 401is moving in a sustained fashion, this reading would have a lower riskratio assigned to it. Further, at step 1107, the cardiopulmonary sensor(n5) may be triggered to check on heart rate, respiration rate, strokevolume, and/or a change in blood pressure. The cardiopulmonary sensor(n5) may thus look for anomalies (e.g., loss of blood) and assign a riskratio to the readings. Or, in other words, the processor 100 may lookfor anomalous readings from the cardiopulmonary sensor n5 and assign arisk ratio to those readings.

At steps 1115, 1116, 1118, and 1119, the processor in the wearabledevice 101 and/or DMS 301 may compare the data from one or more of theabove sensors to determine, e.g., an alert level following thedetermined episode (e.g., impact event). For example, after consideringall of the above weighted data points, the processor may determine thatthe event recorded merits various levels of alerts (at steps 1110 and1113) to be sent to the owner and/or the veterinarian based on thereliability of the sensor readings. Further, the wearable device 101 maybe instructed to continue reading at steps 1110 and 1113 in order tocontinually monitor the animal's progress following the impact event.

The following equations describe the weighting of the values of thesensors and the comparison against the alert level thresholds. Equation(1) below describes how a sensor reading from sensor Nc is checkedagainst the threshold for sensor Nc:

If (n _(c) >n _(c threshold)), then alert for exceeding threshold  (1)

Equation (2) below describes how a sensor reading from sensor Nc ischecked against the threshold for sensor Nc and, if the threshold isexceeded, then determining if a weighted combination of sensor readingsNa and Nb and Nc exceed the alert level 1 threshold:

$\begin{matrix}{\mspace{20mu} {{{If}\mspace{14mu} \left( {n_{c} > n_{c\mspace{14mu} {threshold}}} \right)},{then},\text{}{{{{if}\mspace{14mu} \frac{\left( {n_{a}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 1} \right)}{n_{a\mspace{14mu} {threshold}}} \times w_{a}} + {\frac{\left( {n_{b}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 2} \right)}{n_{b\mspace{14mu} {threshold}}} \times w_{b}} + {\frac{\left( {n_{c}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 3} \right)}{n_{c\mspace{14mu} {threshold}}} \times w_{c}}} \geq a_{1}},\mspace{20mu} {{then}\mspace{14mu} {alert}\mspace{14mu} {for}\mspace{14mu} {alert}\mspace{14mu} 1}}} & (2)\end{matrix}$

where:

-   -   a₁ is the alert level 1 threshold such that a value above a₁        results in alert level 1 while a value below a₁ does not result        in an alert;    -   Times T1, T2, and T3 are the time intervals in which the        previous readings for sensors Na, Nb, and Nc are reviewed; and    -   Wa, Wb, and We are the weighting values for each of the Na, Nb,        and Nc sensor readings.

Notably, equation (2) normalizes the values of each sensor by dividingthe max value of the sensor during a time window (or min as appropriate)by the threshold. This permits the individual units of each sensor tocancel out. Next, the weighting factors scale each normalized sensorreading such that they can be added and compared against the thresholdfor alert level 1 (a1).

Equation (3) below describes a similar analysis as that of equation (2)but sets the alert level threshold at the alert level 2 a2 threshold:

$\begin{matrix}{\mspace{20mu} {{{If}\mspace{14mu} \left( {n_{c} > n_{c\mspace{14mu} {threshold}}} \right)},{then},\text{}{{{{if}\mspace{14mu} \frac{\left( {n_{a}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 1} \right)}{n_{a\mspace{14mu} {threshold}}} \times w_{a}} + {\frac{\left( {n_{b}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 2} \right)}{n_{b\mspace{14mu} {threshold}}} \times w_{b}} + {\frac{\left( {n_{c}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 3} \right)}{n_{c\mspace{14mu} {threshold}}} \times w_{c}}} \geq a_{2}},\mspace{20mu} {{then}\mspace{14mu} {alert}\mspace{14mu} {for}\mspace{14mu} {alert}\mspace{14mu} 2}}} & (3)\end{matrix}$

where:

-   -   a₂ is the alert level 2 threshold such that a value above a₂        results in alert level 2 while a value below a₂ does not result        in an alert;    -   Times T1, T2, and T3 are the time intervals in which the        previous readings for sensors Na, Nb, and Nc are reviewed; and    -   Wa, Wb, and We are the weighting values for each of the Na, Nb,        and Nc sensor readings.

Equation (4a) and (4b) relate to equation (2) but also includes theslave sensor analyses of FIG. 11:

$\begin{matrix}{\mspace{20mu} {{{If}\mspace{14mu} ({master})\mspace{14mu} \left( {n_{a} > n_{a\mspace{14mu} {threshold}}} \right)\mspace{14mu} {and}}\text{}{{{\frac{\left( {n_{a}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 1} \right)}{n_{a\mspace{14mu} {threshold}}} \times w_{a}} + {\frac{\left( {n_{b}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 2} \right)}{n_{b\mspace{14mu} {threshold}}} \times w_{b}} + {\frac{\left( {n_{c}\mspace{14mu} \max \mspace{14mu} {over}\mspace{14mu} {time}\mspace{14mu} T\; 3} \right)}{n_{c\mspace{14mu} {threshold}}} \times w_{c}}} \geq a_{1}}\mspace{20mu} {then}\mspace{14mu} {activate}\mspace{14mu} {slave}\mspace{14mu} \left( {4b} \right)}} & \left( {4a} \right) \\{\mspace{79mu} {{If}\mspace{14mu} \left( {{\left( {\left( {n_{d} < n_{d\mspace{14mu} {low}\mspace{14mu} {threshold}}} \right)\mspace{14mu} {or}\mspace{14mu} \left( {n_{d} > n_{d\mspace{14mu} {high}\mspace{14mu} {threshold}}} \right)} \right)\mspace{14mu} {{or}\mspace{20mu}\left( {\left( {n_{e} < n_{e\mspace{14mu} {low}\mspace{14mu} {threshold}}} \right)\mspace{14mu} {or}\mspace{14mu} \left( {n_{e} > n_{e\mspace{14mu} {high}\mspace{14mu} {threshold}}} \right)} \right)}\mspace{14mu} {{or}\text{}\mspace{20mu}\left( \left( {n_{a} \neq {{preexisting}\mspace{14mu} {profile}\mspace{14mu} {for}\mspace{14mu} n_{a}}} \right) \right)}},\mspace{20mu} {{then}\mspace{14mu} {alert}\mspace{14mu} {level}\mspace{14mu} 2},{{otherwise}\mspace{14mu} {alert}\mspace{14mu} {level}\mspace{14mu} 1.}} \right.}} & \left( {4b} \right)\end{matrix}$

where:

-   -   a₁ is the alert level 1 threshold such that a value above a₁        results in alert level 1 while a value below a₁ does not result        in an alert;    -   Times T1, T2, and T3 are the time intervals in which the        previous readings for sensors Na, Nb, and Nc are reviewed;    -   Wa, Wb, and We are the weighting values for each of the Na, Nb,        and Nc sensor readings; and    -   “preexisting profile for n_(a)” is a profile for expected values        of n_(a) over a time interval.

Here, alert level 2 is defined by being activated by both master andslave reaching predefined levels. Alert level 1 is defined by beingactivated by only the master reaching its predefined level but the slavenot reaching its predefined level.

The equations above also permit the sensors to be located on otherdevices based on the time T being evaluated for each sensor reading. So,once a common time is determined (for instance, the time T(Nc) at whichthe reading from sensor Nc exceeded the Nc threshold), the other sensorreadings are time normalized from that time T(Nc) and evaluated.

Sensors Located on Different Devices

As described above, all of the sensors may be located on wearable device101 or some located on the wearable device 101 and others located on aseparate device. A separate device may be a user's smartphone (e.g., themicrophone on the smartphone). In short, data may be captured andcompared from sensors located on more than one device (e.g., wearabledevice 101 and a user's mobile device) and compared to determine, e.g.,an episodic inference about the animal's health and wellness. Forexample, FIG. 12 illustrates one example method for capturing sensordata from more than one device which can then be forwarded to the DMS301 and analyzed to determine an inference regarding animal's health andwellness (in the depicted example, respiration inferences). As with FIG.11, the timeline 12011 of FIG. 12 indicates a relative time that eachstep is performed relative to one another. In FIG. 12, at step 1201 auser opens a mobile device application. For example, thehealth-monitoring system as described herein may include a companionmobile application that can be downloaded to an animal 401 owner'ssmartphone, tablet, computer, etc., which may capable of triggeringsensors on demand. A user may be the animal's owner or a veterinarian,etc. In step 1202, the user may select a function they wish to collectdata about. The specific sensors selected for capturing and returningdata may vary depending on what particular inference, etc., the usertriggers. In the embodiment depicted in FIG. 12, the user selectsrespiration analysis. At step 1203, commands may be sent to the sensorsto collect and/or forward data related to this respiration analysis. Forexample, because the user selected “respiration analysis,” a command maybe sent to a cardiopulmonary sensor (n5) and to an accelerometer (n3),both located on wearable device 101, and to a microphone (n14) locatedon the user's mobile device. At steps 1204, 1205, and 1206, eachrespective device may collect data and/or retrieve previously collecteddata. These sensors could be placed on standby and triggered based onthe start of an event (as, for instance, a coughing fit).

In the following three examples, the following scenarios are explained:no triggering between the mobile device and the wearable device (onlybeing synced by the DMS), triggering of the mobile device to startrecording by the wearable device, and triggering of the wearable deviceto start recording by the mobile device. In the first example, anapplication executing on the user's mobile device may be executing andrecording audio files with time stamping. The DMS may correlate theaudio file with readings from accelerometers based on time-stamps ofdata obtained from the accelerometers. In the second example, the mobiledevice or the wearable device may trigger the other based on sensedlevels exceeding a threshold. For instance, the mobile device may bewaiting for the wearable device to indicate that the wearable device'saccelerometer has started sensing the coughing fit at which point thewearable device alerts the mobile device. In response to the alert, themobile device may start recording an audio file with time stamps. Inthis example, the excess, uninteresting audio file recorded before thedog started coughing is not recorded. In the third example, the mobiledevice informs the wearable device that the microphone on the mobiledevice has picked up the sounds of the coughing fit and that thewearable device is to monitor the animal. In the following threeexamples, the following scenarios are explained:

Each piece of collected data at steps 1204-1206 may be time-stamped suchthat, when analyzed, each may be lined up in order or otherwisesynchronized to correctly aggregate and consider each piece of data withthe others. At step 1207, the data collected on wearable device 101 isuploaded to the DMS 301, and at step 1208, the data collected at theuser's mobile device is uploaded to DMS 301. At step 1209, the uploadeddata are correlated against each other based on synchronizing thetimestamps to determine when a relevant. Of coughing has begun. Next, instep 1210 the data are analyzed at the DMS 301 to determine appropriateinferences regarding the animal's health and wellness (in the depictedexample, respiration quality).

For example, the combined data may lead to an inference that the animal401 is suffering from kennel cough or bronchitis. Further, because insome embodiments the data will be time-stamped, an inference may bereadily determined even though the sensor readings are coming fromdisparate sources (here, wearable device 101 and a mobile device).Although as described the analysis step 1210 is performed at the DMS301, in other embodiments the analysis may be performed at the user'smobile device and/or the wearable device 101.

In addition to episodic inferences made using the methods depicted inFIGS. 8-12, longitudinal inferences (e.g., trending inferences) may bemade using the above described methods. That is, because collected datamay be stored locally in the wearable device (at, e.g., steps 805, 907,1005/1013, and/or 1109/1112) and/or uploaded to the DMS 301 for storage,changes or fluctuations, etc., in data over time may be monitored, andaccording longitudinal (trending) inferences may be made regardinganimal's health and wellness.

By way of example, in some embodiments animal's long-term weightfluctuations may be monitored and inferences may be made about theanimal 401 accordingly. For example, monitoring long-term weightfluctuations are important as a lean pet has a 15% increase in lifespan(+2 years) and may also be a precursor to other developing conditions.On the other end of the scale, rapid weight loss may be indicative of adigestive track blockage or cachexia where the body is breaking downprotein and fat due to the onset of diabetes. Thus, by monitoring andcomparing an animal's weight overtime, an inference as to the animal'shealth and wellness may be determined.

As another example of a longitudinal inference that may be determinedusing one or more sensors, an activity level of an animal may bemonitored (using, e.g., an accelerometer, GPS, etc.). Further, themeasured activity levels may be adjusted by the DMS 301 for weekends andweekday lifestyle profiles of the animal 401 and/or the animal's owner.For instance, if the owner takes the animal for walks at 3 am, this maybe identified by the owner to the DMS and the DMS refrain from alertingthe owner that the animal has left the owner's house at night.Inferences made from the monitored activity levels may indicate that theanimal is not being provided with enough exercise opportunity or thatconditions such arthritis are slowing the animal down during times ofself-initiated activity.

As another example of a longitudinal inference that may be determinedusing one or more sensors, the animal's eating and hydration habits maybe monitored over time. Hydration and eating fluctuations may beimportant indicators of developing polyphagia and polydipsia conditionsrelated to diabetes.

As another example of a longitudinal inference that may be determinedusing one or more sensors, sleep patterns of an animal may be monitoredto form inferences regarding animal's health and wellness. Sleeppatterns may be important indicators of underlying issues with pets suchas osteoarthritis. Some owners may assume that an animal sleeping moreis just a result of old age, whereas, in reality, it may be an indicatorof developing medical conditions. For example, an animal may not limp orwhine when excited during play and act like a younger dog but will payfor it later. This may manifest itself in longer rests, stiffness onrising, and resistance to go on their regular walks. Other reasons forlonger sleep periods could be caused by thyroid, kidney, or liverdisease. Animals may also have sleep disruption caused byobsessive-compulsive behavior disorders. In some embodiments, sleeppatterns may be derived by the DMS 301 and collaborated with ownerpersonal observations 312.

According to other aspects of the disclosure, longitudinal inferencesmay be determined using the provided UWB technology of the wearabledevice (e.g., using UWB device). For example, in one embodimentrespiration monitoring may uncover abnormal signs such as panting whileresting, using more abdominal muscles to breath, labored breathing,asymmetrical breathing, increased or decreased breathing rates,wheezing, coughing, and choking.

As another example of a longitudinal inference that may be determinedusing UWB technology, animal's heart rate may be monitored over time byUWB device. Heart rate monitoring may uncover increased or decreasedheart rate and/or abnormal rhythms, which may include the heart speedingup and slowing down or missing beats. In additional embodiments, strokevolume measured overtime may be used to derive the overall fitness levelof the animal 401 and/or indicate that the animal 401 is developingconditions that would cause it to be lower.

As another example of a longitudinal inference that may be determinedusing UWB technology, an animal's blood pressure changes (both increasedand decreased blood pressure) may be monitored. Blood pressure changesfrom a base line (which may be measured, e.g., when animal 401 issleeping or otherwise in a state of low activity as discussed) may be anindicator of hypertension developing which may lead to other severemedical conditions.

In any of the above embodiments, collected data may be time-stamped inorder determine time-dependent inferences. That is, time stamping thevarious sensing activities and the ability to look backward in timeallows for a root-cause analysis to determine an adverse event (e.g.,the animal was walking fine, but then played fetch and is now limping).Further, in some embodiments, time-stamping may also allow for theanalysis of the rate of change which in turn can be used to predict apossible outcome (e.g., the animal is running at an increasing rate ofspeed towards the outer area of the geo-zone and thus is likely tobreach that zone).

FIG. 13 presents a table 1301 summarizing illustrative attributes ofsome sensors that may be located on wearable device 101 or locatedexternal to wearable device 101 and used in conjunction with thehealth-monitoring system described herein according to some aspects ofthe disclosure. Specifically, 1301 contains column 1303 denoting anumber of each sensor (denoted as Nm), column 1305 indicating the typeof each sensor, column 1306 describing the location of the sensorrelative to the wearable device, column 1307 indicating a primarypurpose of each sensor, column 1308 describing a general category ofsensor, column 1309 indicating whether each sensor may act as a masteror a slave sensor (as described herein with respect to FIG. 14), column1311 indicating a secondary purpose (if any) of each sensor.

By way of example, in this embodiment N1 refers to a light meter and/orspectrometer located on wearable device 101. As denoted in column 1307,the light meter's primary purpose may be to monitor light levelssurrounding wearable device 101 (and thus animal 401). Further, asindicated in column 1309, the light meter may only act as a slave sensorand thus, in this embodiment, may not control other sensors. Asindicated in column 1311, the light meter may also have a secondarypurpose, here serving as an indoor/outdoor indicator (by, e.g., sensingUV levels) or analyzing nearby chemical signatures in the air.

FIG. 14 presents a table indicating illustrative master/slaverelationships of each sensor presented in FIG. 13 according to or moreembodiments of the disclosure. Specifically, FIG. 14 includes rowsidentifying each sensor as well as columns identifying each sensor. Thevalues in each cell identify the relationship as a row sensor is amaster sensor in contrast to the slave identified in the column sensorwhere the intersecting cell includes an “X”. At the intersection of thesame sensor in the row and column title, the cell value is identified by“I” to indicate if the identical sensor. Interestingly, in someimplementations, each sensor may act as a master to itself (e.g.,control further collection of data by itself in response to a sensedreading). An example of this is shown in step 1120 of FIG. 11identifying whether the readings from sensor Nc are outside of anexpected profile.

By way of example, as indicated by each “X” or darkened cell in the rowfollowing “N3” listed, in some embodiments accelerometer (N3) may act asa master to slave sensors N1 (light meter), N2 (peak sound), N3 (itself,accelerometer), N4 (GPS), N5 (cardiopulmonary), N6 (temperature), N8(Wi-Fi), N9 (Bluetooth), N10 (RF), and N11 (GSM). Further, as indicatedby each “X” or darkened cell in the column below “N3”, in someembodiments accelerometer (N3) may serve as a slave to other mastersensors, namely N3 (itself, accelerometer), N5 (cardiopulmonary), N13(battery strength), and N14 (mobile microphone).

FIG. 15 relates to various operation modes and how each sensor mayoperate in the various operation modes. Column 1501 identifies thesensor by number. Column 1502 identifies a sensor type. Column 1503identifies how each sensor operates in a profile operation mode. Column1504 identifies how each sensor operates in an airplane (no RF radiosoperative) operation mode. Column 1505 identifies how each sensoroperates in a location alert operation mode.

For instance, FIG. 15 identifies the peak sound sensor, theaccelerometer, and the time of day sensor (e.g., an internal clock) arenot affected by the specific profile settings when in the profile modeas shown in column 1503. The remaining sensors may have differentoperations based on the profile.

In the airplane operation mode 1504, most of the sensors are off whilepeak sound is in a standby state the accelerometer, the ambienttemperature sensor, and the time of day sensor are on. In other words,the operation of the sensors in the airplane mode identifies that allradios, sensors, and/or components that generate significant thatgenerate significant electro-magnetic radiation are disabled.

In the location alert operation mode 1505, all sensors that may helpdetermine the location of an animal are on, including light meter,accelerometer, GPS, WiFi signal detector, Bluetooth signal detector, RFsignal detector, and GSM signal detector sensors. The remaining sensorsmay be turned off to help conserve power. The battery strength sensormay also be left on in the location alert mode 1505 to identify to thecollar when it is running low on power. For example, the cardiopulmonarysensor n5 is disabled in favor of the GPS sensor/radio n4, the Wi-Fisensor/radio n8, the Bluetooth sensor/radio n9, the RF sensor/radio,n10, and the GSM sensor/radio n11, depending on which of thesesensors/radios are present.

FIGS. 16A-16G relate to different profiles usable by wearable device101. In each of FIGS. 16A-16G, column 1601 identifies the sensor numberand columns 1602 identifies the sensor type.

FIG. 16A describes a first profile, Profile 0, which relates to a normalmonitoring profile set by the owner. The profile type identified in cell1603A and its title identified in cell 1604A. Here, the range betweenthe low threshold 1605A and the high threshold 1606A is set relativelylarge, the frequency of operation of each sensor is relativelyinfrequently, and granularity for the readings of various sensors islow. This profile is an example of a normal profile set by the owner.For instance, a processor operating under Profile 0 of FIG. 16A has alow granularity for accelerometer sensor n3. The low granularity maytake the form of a low pass filter applied to a signal from theaccelerometer sensor n3. The low pass filter may smooth anyinstantaneous accelerometer output level to eliminate and/or reduce thetriggering of the accelerometer high threshold when the instantaneousoutput is above the high threshold but while the average output is low.Alternatively, the low pass filter may be replaced with a smoothingfilter (e.g., a convolution filter with a longer time constant) toreduce any errant spikes in the signal from the accelerometer n3.Further, the above described filters may be part of the processor suchthat the processor ignores or is less sensitive to acceleration spikeswith short duration

FIG. 16B describes a second profile, Profile 1, which relates to anenhanced monitoring profile set by the owner. The profile typeidentified in cell 1603B and its title identified in cell 1604B. Here,the range between the low threshold 1605B and the high threshold 1606Bis narrow compared to that of Profile 0 of FIG. 16A, the frequency ofoperation of each sensor is relatively more frequent, and granularityfor the readings of various sensors is high. This profile is an exampleof an enhanced profile where the owner is concerned about the pet'scurrent health and desires more information to be obtained by thecollar. In contrast to the Profile 0 of FIG. 16A, this Profile 1 hasenhanced sensitivity as shown in some of the trigger point for the lowthresholds of column 1605B being higher and the trigger point for thehigh thresholds of column 1606B being lower. Also in some instances, thefrequency of monitoring in column 1601B is more often. Similarly, thegranularity as shown in column 1608B is also high. For instance, foraccelerometer n3, the granularity is described in column 1608B as beinghigh. With respect to the example of the low pass filter, the filter maybe removed or modified to reduce the level of filtering of higherfrequency signals. With respect to the example of the smoothing filter,the time constant (or window of time over which the smoothing takesplace) is reduced to permit higher frequency acceleration signals to beanalyzed by a processor. Also, as described with respect to FIG. 16A,the filters may be part of the processor such that the processor adjustsinternally how sensitive it is to the outputs of various sensors basedon a current profile.

FIG. 16C describes a third profile, Profile 2, which relates to a normalmonitoring profile set by the veterinarian. The profile type identifiedin cell 1603C and its title identified in cell 1604C. Here, the rangebetween the low threshold 1605C and the high threshold 1606C is setrelatively large with even some sensors not being used as theveterinarian may not need the readings from the sensors, the frequencyof operation of each sensor is relatively infrequently, and granularityfor the readings of various sensors is low. This is an example of aprofile where the vet may be monitoring the pet's current health toestablish a baseline or as a function of general monitoring (forexample, in preparation for a checkup).

FIG. 16D describes a fourth profile, Profile 3, which relates to anenhanced monitoring profile set by the veterinarian. The profile typeidentified in cell 1603D and its title identified in cell 1604D. Here,the range between the low threshold 1605D and the high threshold 1606Dis set relatively narrow, the frequency of operation of each sensor isrelatively frequent, and granularity for the readings of various sensorsis high. Again here, some sensors are disabled as the veterinarian mayhave no need for the readings from those sensors. For instance, thisprofile may be used before surgery or a procedure (e.g., teeth cleaningwith the animal being anesthetized) is performed on the animal to ensureno recent dramatic events have occurred to the animal prior to thesurgery/procedure.

For instance, this profile may be used after surgery or after aprocedure to monitor for possibility of complications arising from thesurgery. Based on the level of need for monitoring the animal, the rateat which information is provided to the veterinarian may be furthermodified in accordance with the examples of FIG. 22 as relating to thefollowing:

-   A. Identification of events by the wearable device and uploading    those events to the veterinarian,-   B. Logging of raw data from the sensors and batch uploads of the    logged data to the veterinarian, or-   C. Continuous uploads of raw data to the veterinarian.

With respect to the above description and the description of FIG. 22,the uploads of the identified events and/or raw data to the veterinarianmay be a direct transfer from the wearable device to a remote device(for instance, to a computer on a same local Wi-Fi network as thewearable device) or may be an indirect transfer from the wearable deviceto the DMS which then forwards to the veterinarian (or makes availablefor the veterinarian to access) the identified events and/or raw datafrom the wearable device. Further, the DMS may further derived eventsfrom the raw data and possibly the device-derived events from thewearable device. These DMS-derived events may be further provided to theveterinarian or made available for viewing by the veterinarian asdesired.

FIG. 16E describes a fifth profile, Profile 4, which relates to amonitoring profile for a first specific symptom type as set by theveterinarian. The profile type identified in cell 1603E and its titleidentified in cell 1604E. Here, the range between the low threshold1605E and the high threshold 1606E is set relatively narrow, thefrequency of operation of each sensor is relatively frequent, andgranularity for the readings of various sensors is high for some sensorsbut low for others. In this profile, the veterinarian is concentratingon values from some sensors over other sensors. For instance, theveterinarian may be monitoring for gait-related issues based on theaccelerometer frequency sampling being “always on” and the granularitybeing “high”.

FIG. 16F describes a sixth profile, Profile 5, which relates to amonitoring profile for a second specific symptom type as set by theveterinarian. The profile type identified in cell 1603F and its titleidentified in cell 1604F. Here, the range between the low threshold1605F and the high threshold 1606F is set relatively narrow, thefrequency of operation of each sensor is relatively frequent, andgranularity for the readings of various sensors is high for some sensorsbut low for others. In this profile in contrast to that of Profile 4,the veterinarian is concentrating on values from a difference of sensorsthen important sensors of Profile 4 of FIG. 16E. Here, the veterinarianmay be monitoring for a cardiopulmonary-type symptoms or similar set ofsymptoms by the cardiopulmonary sensor n5 frequency being set to obtaina reading every minute with its granularity set to high.

FIG. 16G describes a seventh profile, Profile 6, which relates to anenhanced monitoring profile set by the veterinarian in which somesensors are operated continuously as opposed to their standardintermittent usage. The profile type identified in cell 1603G and itstitle identified in cell 1604G. Here, the range between the lowthreshold 1605A and the high threshold 1606A is set relatively arrow,the frequency of operation of each sensor depends on its importance. Forthose sensors that are not important, they are not operated and incontrast other sensors are operated continuously. For instance, thisprofile may be used when an animal is recovering from surgery and theveterinarian desires continuous readings of the vitalsigns/physiological signs of the animal without stressing the animal byhaving individual sensors for each vital sign/physiological sign beingseparately attached. Alternatively, this profile may be used when theanimal is in critical condition and is in a constantly monitored state.In this profile, some items are not monitored as they are not relevantwhen staying in hospital. For instance, monitoring the ambienttemperature via sensor n6 or monitoring for GPS signals with sensor n4are not needed. This profile of FIG. 16G enables veterinarians to usethe wearable device 101 in place of separately attached individualsensors that would normally be attached individually to the animal.

FIG. 17 shows an example of how various sensor profiles may be modifiedbased on breed information of the animal to which the monitoring deviceis attached in accordance with one or more aspects of the disclosure.Specifically, column 1701 identifies those sensors that may be modifiedor adjusted in sensitivity when processing based on the type of breed ofanimal. For instance, high and low thresholds for cardiopulmonary sensorn5 may be adjusted upwards for a breed that has a high average heartrate and downwards for a breed that has a low average heart rate.

FIG. 18 shows an embodiment with different operation modes of thewearable device in accordance with one or more aspects of thedisclosure. In this embodiment, the wearable device operates in one ofthree operation modes: a profile mode 1802, an airplane mode 1803, and alocation alert mode 1804. The collection of operation modes is shown asgroup 1801 and the collection of profiles are shown as group 1802. Inthis embodiment, two profiles may be implemented in the wearable device:owner profile 1805 and veterinarian/third-party profile 1806. Based onthe selection of the operation mode, wearable device 1807 operates asdesignated by the particulars of the operation mode. Finally, based onthe designation in the operation mode of what and when to upload contentto the remote data management system, the wearable device 1807 uploadscontent in accordance with the operation mode.

For instance, in the profile operation mode 1802, this operation mode(and optionally the specific profile) identifies that content from thewearable device 1807 is to be uploaded to the remote data managementsystem 1808 in batches. Next, in the airplane operation mode 1803, asall radio transmission functions are disabled while in the airplaneoperation mode 1803, the content collected while in operation mode 1803is stored in wearable device 1807 and subsequently uploaded to remotedata management system 1808 only when switched out of airplane mode1803. Further, when operating in the location alert operation mode 1804,content information is uploaded to the remote data management system1808. For instance, in one example where the owner is attempting tolocate the animal as soon as possible, the location content may beuploaded on a continuous basis to the remote data management system1808. The data uploaded from the wearable device may include locationinformation from a GPS receiver sensor and/or triangulation informationfrom received cell tower signal strengths and/or IP addresses of Wi-Fiaccess points, merely storing a list of time stamped IP addresses, orthe like. The uploading of data may be real-time or may be batched. Withrespect to monitoring Wi-Fi access points, the wearable device 101 maykeep track of the various access points encountered over time and uploada list of those access points so as to provide a list of locations (orapproximate locations) visited throughout the day (or other interval)(thereby providing breadcrumb information of where the wearable devicehas been throughout the day).

FIGS. 19A-19B show the order in which operation modes take precedenceover profiles based on the embodiment of FIG. 18 in accordance with oneor more aspects of the disclosure. As used in FIGS. 19A-19B, the“switches” can be hardware switches, software switches or a combinationof both. A hardware switch may be a switch located locally on thewearable device that permits selection of one of the operation modesdescribed in FIG. 18. A software switch is a remotely operated commandto the wearable device to shift into one of the operation modes of FIG.18 and/or profiles. The software switch maybe operated by the owner, aveterinarian, and or a third party. For instance, airport personnel maybe included in the group including the third-party where the airportpersonnel may be able to access the wearable device to set it into theairplane operation mode 1803. The combination of hardware and softwareswitches permits a device to respond to either a hardware switchoperation (actual switch or a double tap of the device—sensed by theinternal accelerometer) or a software switch operation. For instance,external hardware switches may be located at one or more locations onthe wearable device 101 at, for instance, locations A-C on the wearabledevice 101 of FIG. 5 or as part of collar/harness 402. Here, thehardware switches may be respective parts of clasp 505 at locations Hand I and operated by locking together the parts of clasp 505.

FIG. 19A shows a deprecated order in which an airplane mode switch 1901has the highest level of precedence. Next, a location alert switch 1902has the second-highest level precedence. Third, the lowest level ofprecedence is profiles in profile group 1903 including owner profile1904 and veterinarian/third-party profile 1905.

FIG. 19B shows the different operation modes based on operation of theswitches of FIG. 19A. First, if the airplane mode switch is on, then thewearable device operates in the airplane mode 1907. If the airplane modeswitch is off 1906, then the wearable device looks to the state of thelocation alert switch. If the location alert switch is on, then thewearable device operates in the location alert operation mode 1909. Ifthe location alert switch is off 1908, then the wearable device operatesin one of the profile modes 1910 (for instance, in the owner profile1911 or the veterinarian/third-party profile 1912).

FIG. 20 shows an alternative embodiment with different profilesincluding profiles replacing the operation modes of the embodiment ofFIG. 18 in accordance with one or more aspects of the disclosure.Profiles 2001 include airplane profile 2004, location alert profile2005, owner profile 2002, and veterinarian/third-party profile 2003. Theselected profile from profiles 2001 dictate how wearable device 2006operates and uploads data to the remote data monitoring system 2007(similar to the operation mode/profiles of FIG. 18).

FIGS. 21A-21B show the combination of different profiles of theembodiment of FIG. 20 with options of profile selection by one or moreswitches in accordance with one or more aspects of the disclosure. FIGS.21A-21B described profiles being designated byhardware/software/combination switches (the switches having beendescribed with respect to FIGS. 19A-19B). In FIG. 21A, the collection ofprofiles 2101 includes owner profile 2102, veterinarian/third-partyprofile 2103, airplane mode profile 2104, and location alert profile2105. FIG. 21B shows the collection of profiles 2110 with the airplanemode switch and the locations mode switch designating at least some ofthe profiles. For instance, when airplane mode switch 2112 is on, thewearable device operates in airplane mode profile 2113. When airplanemode switch is off 2111, the location alert switch status is checked. Ifthe location alert switch is on 2115, the wearable device operates inthe location alert profile 2118. If the location alert switch is off2114, the wearable device operates in one of the owner profile 2116 orthe veterinarian/third-party profile 2117 (as separately designated bythe owner and/or veterinarian/third-party).

FIG. 22 shows an example of how profiles may be selected in the wearabledevice as well as in the DMS in accordance with one or more aspects ofthe disclosure. Wearable device 2201 shown relative to DMS 2213. At step2202, an initial profile is set for the wearable device 2201. In step2203, it is determined whether a sensor or combination of sensors hasexceeded one or more thresholds as described herein. If yes, then thewearable device modifies its own profile to change to a differentprofile or operation mode as shown in step 2204. Also, as shown by theyes arrow extending down from step 2203, the derived events may beuploaded to the DMS in step 2205, raw data may be uploaded to the DMS inbatches as shown in step 2206, or raw data may be continuously uploadedto the DMS in step 2207 depending on the new profile or new operationmode. If no from step 2203, the derived events may be uploaded to theDMS in step 2205, raw data may be uploaded to the DMS in batches asshown in step 2206, or raw data may be continuously uploaded to the DMSin step 2207 depending on the current profile or current operation mode.

Next, content from wearable device 2201 is received at the DMS 2213 atstep 2208. In step 2209, the data is stored (for instance, in a databasein one or more servers with dynamic or solid-state memory as shown bydatabase 2210) and subsequently analyzed. If in step 2211, an alert hasbeen triggered from the analyzed data, then DMS 2213 instructs wearabledevice 2201 to change to a different profile or operation mode inaccordance with the alert level determined in step 2211. Alternatively,if no from step 2211, no alert has been determined and the DMS 2213continues to monitor for content from wearable device 2201 in step 2208.

FIG. 23 shows an example of how output from various sensors may bestored for an interval of time and then discarded in accordance with oneor more aspects of the disclosure. FIG. 23 shows the past history forsignals from accelerometer 2301, light sensor 2302, and sound sensor(microphone) 2303. In this example, older readings 2309 fromaccelerometer 2301 were below an accelerometer threshold level{Threshold(acc)}. However more recently, the signal from theaccelerometer rose to level 2308, which is above {Threshold(acc)}.

As described above, processor 100 may then evaluate previous readingsfrom other sensors. Previous values from light sensor 2302 areevaluated. Looking back in the recent history of the values from lightsensor 2302, the readings were originally at level 2311, which is belowthe light threshold {Threshold(light)}. However, more recently, thelight level rose to the level at 2310. As this level at 2310 is abovethe light threshold {Threshold(light)}, the values from the light sensorcorroborate the event that may be have been detected by accelerometer2301. With respect to sound level, older sound level readings were atlevel 2315, which were below the sound threshold {Threshold(sound)}.More recently, the sound level rose to level 2314, which is above thesound threshold{Threshold(sound)}. Here, the output from the soundsensor also corroborates event that may have been detected byaccelerometer 2301.

With respect to both the light sensor 2302 and sound sensor 2303, anindividual signal value different from a maximum value above a thresholdhaving been reached during a time interval is less relevant than thesignal having reached the threshold during the time window. Stateddifferently, once it has been determined that a light signal is abovethe light threshold {Threshold(light)} for sensor reading 2310, otherreadings between levels 2312 and 2313 are not considered for thisthreshold analysis. Similarly, variants between sound level 2316 and2317 are less relevant than the sound level 2314 having passed the soundthreshold level {Threshold(sound)} as the sound threshold has alreadybeen met.

Finally, FIG. 23 shows data dump points 2305, 2306, and 2307 after whichinsignificant signal readings are dumped from the memory of processor100 and/or storage 105. Interestingly, the data dump points 2305, 2306,and 2307 do not have to be at the same time window from the present.Rather each may have its own separate window length during which signallevels are maintained.

FIG. 24 shows an example of different techniques for monitoring coretemperature including microwave radiometry and microwave thermometry inaccordance with one or more aspects of the disclosure. For instance,core temperature 2401 may be determined through passive technologiesincluding microwave radiometry 2402 in which energy from other sourcesis used to determine core temperature. Also, active techniques includingmicrowave thermometry 2403 may be used to determine core temperature.For these two examples, separate antennas may be used for ultra-widebanddevice (UWB) and the microwave radiometry/thermography core temperaturedetermination system as shown by state 2404. Alternatively, a singleantenna may be shared between the UWB and the core temperaturedetermination device. For example, one or more switches may be used toalternatively connect the shared antenna to the UWB in the microwaveradiometry/thermography core temperature determination system as shownby state 2405.

Owner's User Interface

FIGS. 25 and 26 show illustrative examples of an owner's user interfaceas displayable on a computer or smart phone. The Owner Health & WellnessDashboard allows the owner to see in one place all trending informationon the animal from sensor data and DMS derived data.

FIG. 25 shows a display 2501 of various information and conditions of amonitored animal in accordance with aspects of the disclosure. Thedisplay includes information drawn from both the wearable device 101 aswell as from content from the veterinarian. For instance, informationfrom the veterinarian includes the next scheduled appointment content2502 and the identification of what medications are expiring next andthe expiration dates. This information may help remind the user to keepthe veterinarian appointment.

Next, the display 2501 includes content from the wearable device and/orthe DMS in the form of instantaneous vital signs/physiological signswere overall trends relevant to the animal. For instance, display 2501includes graphical indicators of activity 2505, sleep 2506, hydration2507, diet 2508, stress 2509, core temperature 2510, weight 2511, heartrate 2512, and respiration rate 2513. The following items relate toinstantaneous vital signs/physiological signs from the wearable device:core temperature 2510, heart rate 2512, and respiration rate 2513.

In contrast to the vital signs, the following items relate to wearabledevice-derived events or DMS-derived events such that they incorporatecontent from different sensors and may include tracking ofhealth-related vital signs/physiological signs and/or activities overtime: activity 2505, sleep 2506, hydration 2507, diet 2508, stress 2509,and weight 2511.

For purposes of illustration, each of the graphical displays of theseitems is shown as a dial with an arrow pivoting from one side of thedial to the other based on the state of the displayed item (e.g., agreen area indicating no concern, a yellow area indicating caution, anda red area indicating concern for that individual item).

FIG. 26 shows activity level for that particular animal in accordancewith aspects of the disclosure. The Owner Level Detail screen allows theowner to drill down on a specific item from the dashboard and reviewgoals, alerts, recommendations, and more detailed, long term analysesinformation. For instance, the display 2601 of FIG. 26 includes anidentification of the animal 2602, a current indicator 2603 for thedetail screen (in this example, the activity of the animal), and analert message box 2604 identifying an alert determined by the wearabledevice 101 and or the DMS 301 (in this example that the animal missedtwo consecutive days of walks with an identification of the date andtime of when the walks were missed). Next, the display 2601 may furtherinclude recommendations in field 2605 to improve the health of theanimal (for instance, to resume daily walks). The display 2601 mayinclude one or more goals as set by the veterinarian, the owner, or theDMS 301. In this example, the goals are to walk 40 minutes per day, tokeep the animal's weight below 80 pounds and to play 15 minutes. Thedisplay 2601 may further include an identification of the alertthresholds in field 2608. In this example, the alert thresholds aremissing two days of a walk, a change in gait dropping 15%, and anoverall drop in activity of 25%.

Finally, a timeline of the displayed item of detail may be shown ascontent 2607. Here, the timeline shows how the animal's activity levelhas changed over 12 weeks.

While the detailed screen 2601 of FIG. 26 relates to activity, it isappreciated that similar detail screens may be provided for other itemsidentified in FIG. 25 with similar content including a graphicalindication of the current status of that item, alerts, recommendations,goals, alert thresholds, and timelines.

Although example embodiments are described above, the various featuresand steps may be combined, divided, omitted, and/or augmented in anydesired manner, depending on the specific secure process desired. Thispatent should not be limited to the example embodiments described, butrather should have its scope determined by the claims that follow.

1. A wearable device comprising: a processor; an accelerometer; anultra-wideband (UWB) radar system including a first inward-facingantenna through which a UWB signal is transmitted and a secondinward-facing antenna through which a return UWB signal is received; andmemory storing executable instructions that, when executed, cause theprocessor to: receive a signal from the accelerometer; weight the signalreceived from the accelerometer; determine, based on the weightedsignal, whether a probability that an event has occurred is over athreshold associated with the event; based on determining that theprobability that the event has occurred is over the threshold associatedwith the event: generate a UWB signal; receive a return UWB signal;determine whether the return UWB signal is associated with a condition;and based on determining that the UWB signal indicates the condition,generate an alert.
 2. The wearable device of claim 1, comprising aglobal positioning system (GPS) receiver, wherein the executableinstructions, when executed, cause the processor to: determine, usingthe GPS receiver, a location of the wearable device; and send a messagecomprising the location of the wearable device.
 3. The wearable deviceof claim 1, wherein the executable instructions, when executed, causethe processor to: determine respiration issues associated with an animalwearing the wearable device; and send an alert regarding the respirationissues associated with the animal wearing the wearable device.
 4. Thewearable device of claim 1, wherein the executable instructions, whenexecuted, cause the processor to: determine a respiratory rate of ananimal wearing the wearable device.
 5. The wearable device of claim 1,wherein the executable instructions, when executed, cause the processorto: determine a heart rate of an animal wearing the wearable device. 6.The wearable device of claim 1, comprising a battery sensor, wherein theexecutable instructions, when executed, cause the processor to:determine a charge level of a battery of the wearable device; and send amessage comprising the charge level of the battery.
 7. The wearabledevice of claim 6, wherein the battery of the wearable device comprisesa rechargeable battery.
 8. The wearable device of claim 1, comprising aWi-Fi radio, wherein the executable instructions, when executed, causethe processor to: send the alert via the Wi-Fi radio.
 9. The wearabledevice of claim 1, comprising a cellular radio, wherein the executableinstructions, when executed, cause the processor to: send the alert viathe cellular radio.
 10. The wearable device of claim 1, wherein theexecutable instructions, when executed, cause the processor to: send thealert via email, text message, or phone call.
 11. The wearable device ofclaim 1, wherein the executable instructions, when executed, cause theprocessor to: determine distress of an animal wearing the wearabledevice; and send the alert based on determining the distress of theanimal wearing the wearable device.
 12. The wearable device of claim 1,wherein the executable instructions, when executed, cause the processorto: determine whether an animal wearing the wearable device is layingdown.
 13. The wearable device of claim 1, wherein the executableinstructions, when executed, cause the processor to: determine, based onsensor data collected by a sensor of the wearable device, a risk ratioassociated with an animal wearing the wearable device.
 14. The wearabledevice of claim 1, wherein the executable instructions, when executed,cause the processor to: compare sensor data collected by a sensor of thewearable device to a second threshold; and based on determining that thesecond threshold has been met, send the alert.
 15. The wearable deviceof claim 1, wherein the executable instructions, when executed, causethe processor to: store a first profile corresponding to a first animalthat may wear the wearable device; and store a second profilecorresponding to a second animal that may wear the wearable device. 16.The wearable device of claim 1, wherein the executable instructions,when executed, cause the processor to: analyze sensor data collected byone or more sensors of the wearable device.
 17. The wearable device ofclaim 1, wherein the executable instructions, when executed, cause theprocessor to: determine baseline data corresponding to an animal wearingthe wearable device.
 18. The wearable device of claim 1, wherein theexecutable instructions, when executed, cause the processor to: receivea software update for software of the wearable device; and apply thesoftware update to the software of the wearable device.
 19. A wearabledevice comprising: a processor; a sensor; an ultra-wideband (UWB) radarsystem including a first antenna through which a UWB signal istransmitted and a second antenna through which a return UWB signal isreceived; and memory storing executable instructions that, whenexecuted, cause the processor to: receive sensor data collected by thesensor; determine, based on the sensor data, whether a probability thatan event has occurred is over a threshold associated with the event;based on determining that the probability that the event has occurred isover the threshold associated with the event: generate a UWB signal;receive a return UWB signal; determine whether the return UWB signalindicates a condition; and based on determining that the UWB signalindicates the condition, generate an alert.
 20. A wearable devicecomprising: a processor; a global positioning system (GPS) receiver; anaccelerometer; a communication interface; an ultra-wideband (UWB) radarsystem including a first antenna through which a UWB signal istransmitted and a second antenna through which a return UWB signal isreceived; and memory storing executable instructions that, whenexecuted, cause the processor to: receive sensor data collected by thesensor; determine, based on the sensor data, whether a health conditionof an animal wearing the wearable device is over a threshold associatedwith the health condition; and based on determining that the healthcondition is over the threshold associated with the health condition,send an alert via the communication interface.